Complex disease function specific core marker construction method and related equipment

Abstract

The embodiment of the invention provides a method for constructing a core marker with specific functions of complex diseases and related equipment, and belongs to the technical field of biological detection. The method comprises the following steps: acquiring biological knowledge data related to a target disease; extracting candidate genes associated with a target disease, target function set data, a disease associated marker set and disease transcriptomics data from the biological knowledge data; performing disease relevance measurement on the candidate genes according to the target function set data, the disease relevance marker set and the disease transcriptomics data to obtain disease relevance measurement data of each candidate gene; screening the candidate genes according to the disease relevance measurement data to obtain selected genes; and marking the target disease according to the selected gene to obtain the special function core marker of the target disease. According to the embodiment of the invention, the construction cost of the core marker specific to the complex disease function is lower, and the accuracy is higher.

Description

Technical Field

[0001] The present application relates to the field of biological detection technology, and in particular to a method for constructing a core marker specific to complex disease functions and related equipment. Background Art

[0002] Computer technology and biology are integrated for complex disease research. In complex disease research, it is necessary to analyze the omics data of complex diseases in order to treat complex diseases based on the omics data. However, in related technologies, it is necessary to screen out core markers specific to the functions of complex diseases from a large number of markers. The number of markers identified is too high, and it is impossible to determine whether they are related to complex diseases, making the construction of core markers for complex diseases complex and low in accuracy. Therefore, how to improve the accuracy and simplicity of the construction of core markers specific to the functions of complex diseases has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The main purpose of the embodiments of the present application is to propose a method for constructing core markers specific to complex disease functions and related equipment, aiming to make the construction of core markers specific to complex disease functions more accurate and simple.

[0004] To achieve the above objectives, the first aspect of the embodiments of the present application proposes a method for constructing a core marker specific for complex disease functions, the method comprising:

[0005] Acquire biological knowledge data related to target diseases;

[0006] Extracting candidate genes, target function set data, disease-associated marker sets and disease transcriptomics data associated with the target disease from the biological knowledge data; wherein the target function set data is function set data consisting of function names, RNA targeting relationships and gene genetic variation results associated with the target disease;

[0007] Performing disease association measurement on the candidate genes according to the target function set data, the disease association marker set and the disease transcriptomics data to obtain disease association measurement data of each candidate gene;

[0008] Screening the candidate genes according to the disease association measurement data to obtain selected genes;

[0009] The target disease is marked according to the selected gene to obtain a core marker of the target disease.

[0010] In some embodiments, the target function set data includes: a functional gene set, a targeted gene set and a function loss intolerance degree set, wherein the functional gene set represents a gene set with a function related to the target disease, the targeted gene set represents a gene set with a targeting relationship with the RNA of the target disease, and the function loss intolerance degree set includes the function loss intolerance degree of each candidate gene; the disease association degree of the candidate gene is evaluated according to the target function set data, the disease association marker set and the disease transcriptomics data to obtain the disease association measurement data of each candidate gene, including:

[0011] Performing pathogenicity measurement on each of the candidate genes according to the disease-associated marker set to obtain pathogenicity measurement data;

[0012] Performing functional contribution measurement on each candidate gene according to the functional gene set to obtain functional contribution measurement data;

[0013] Perform differential expression measurement on each of the candidate genes according to the disease transcriptomics data to obtain differential expression measurement data;

[0014] Performing gene targeting measurement on each of the candidate genes according to the targeted gene set to obtain gene targeting measurement data;

[0015] Performing a function loss intolerance measurement on each of the candidate genes according to the function loss intolerance degree set to obtain function loss intolerance measurement data;

[0016] The pathogenicity metric data, the functional contribution metric data, the differential expression metric data, the gene targeting metric data and the loss-of-function intolerance metric data are constructed into disease association metric data for each of the candidate genes.

[0017] In some embodiments, performing differential expression measurement on each of the candidate genes according to the disease transcriptomics data to obtain differential expression measurement data includes:

[0018] Acquire the data category of the disease transcriptomics data;

[0019] Preprocessing the disease transcriptomics data according to the data category to obtain candidate transcriptomics data;

[0020] Performing matrix conversion on the candidate transcriptomics data to obtain a transcriptomics matrix; wherein each row in the transcriptomics matrix represents a gene, and each column represents a sample;

[0021] The transcriptomic matrix is ​​subjected to differential expression measurement to obtain the differential expression measurement data.

[0022] In some embodiments, performing differential expression measurement on the transcriptomics matrix to obtain the differential expression measurement data includes:

[0023] Extracting disease group expression values ​​and normal group expression values ​​from the transcriptomic matrix;

[0024] Obtaining a differential expression value between the disease group expression value and the normal group expression value;

[0025] The differential expression values ​​are combined to obtain a target expression value;

[0026] Determining grade data according to the target expression value and a preset expression threshold;

[0027] The level data are processed by mean value to obtain the differential expression measurement data.

[0028] In some embodiments, the targeted gene set includes: a miRNA targeted gene set, a LncRNA targeted gene set and an RNA relationship set; performing gene targeting evaluation on each of the candidate genes according to the targeted gene set to obtain gene targeting metric data includes:

[0029] Performing a targeting measurement on the candidate gene according to the miRNA targeted gene set to obtain first targeting measurement data;

[0030] Performing a targeting measurement on the candidate gene according to the LncRNA targeted gene set to obtain second targeting measurement data;

[0031] Performing targeting relationship measurement on the candidate genes according to the miRNA targeting gene set, the LncRNA targeting gene set and the RNA relationship set to obtain targeting relationship measurement data;

[0032] The first targeting metric data, the second targeting metric data and the targeting relationship metric data are spliced ​​to obtain the gene targeting metric data.

[0033] In some embodiments, performing a function loss intolerance measurement on each of the candidate genes according to the function loss intolerance degree set to obtain the function loss intolerance measurement data comprises:

[0034] Obtaining the gene name of the candidate gene;

[0035] A loss-of-function intolerance probability search is performed in the loss-of-function intolerance degree set according to the gene name to obtain the loss-of-function intolerance measurement data.

[0036] In some embodiments, screening the candidate genes according to the disease association metric data to obtain selected genes includes:

[0037] Sorting the candidate genes in descending order according to the disease association measurement data to obtain a ranking number for each candidate gene;

[0038] The selected genes are extracted from the candidate genes according to a preset gene selection quantity and the sorting sequence number.

[0039] To achieve the above-mentioned purpose, the second aspect of the embodiment of the present application proposes a device for constructing a core marker specific to complex disease functions, the device comprising:

[0040] A data acquisition module is used to obtain biological knowledge data related to the target disease;

[0041] An extraction module, used to extract candidate genes associated with the target disease, target function set data, disease-associated marker set and disease transcriptomics data from the biological knowledge data; wherein the target function set data is function set data composed of function names associated with the target disease, RNA targeting relationships and gene genetic variation results;

[0042] A correlation measurement module, used for performing disease correlation measurement on the candidate genes according to the target function set data, the disease-related marker set and the disease transcriptomics data, to obtain disease correlation measurement data of each candidate gene;

[0043] A screening module, used to screen the candidate genes according to the disease association measurement data to obtain selected genes;

[0044] The marking module is used to mark the target disease according to the selected gene to obtain the specific functional core marker of the target disease.

[0045] To achieve the above objectives, a third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the method described in the first aspect is implemented.

[0046] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in the first aspect is implemented.

[0047] The method and related equipment for constructing core markers specific to complex disease functions proposed in this application extract biological knowledge data related to the target disease from existing biological databases / literatures, and the biological knowledge data extracted from different biological databases / literatures include candidate genes associated with the target disease, target function set data, disease-associated marker sets, and disease transcriptomics data. The target function set data, disease-associated marker sets, and disease transcriptomics data are used to analyze the association between each candidate gene and the target disease to obtain disease association measurement data, so as to screen selected genes that play an important role in the occurrence and development of the target disease from the candidate genes through the disease association measurement data, so as to use the selected genes as the core markers of the function of the target disease. Therefore, the core markers specific to the function of the target disease are constructed through the existing public background knowledge, and the target function set data, disease-associated marker sets, and disease transcriptomics data in the public background knowledge are used to analyze the association between genes and diseases, so that the cost of constructing core markers specific to the function of the target disease is reduced and accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 It is a flow chart of the method for constructing core markers specific for complex disease functions provided in the examples of the present application;

[0049] Figure 2 yes Figure 1 Flow chart of step S103 in FIG.

[0050] Figure 3 yes Figure 2 Flow chart of step S203 in FIG.

[0051] Figure 4 yes Figure 3 Flow chart of step S304 in FIG.

[0052] Figure 5 yes Figure 2 Flow chart of step S204 in FIG.

[0053] Figure 6 yes Figure 2 Flow chart of step S205 in FIG.

[0054] Figure 7 yes Figure 1 Flow chart of step S104 in FIG.

[0055] Figure 8 This is a flow chart of a method for constructing a core marker specific for complex disease functions provided in another embodiment of the present application;

[0056] Fig. 9 It is a schematic diagram of the structure of a complex disease function-specific core marker construction device provided in an embodiment of the present application;

[0057] Fig.10 It is a schematic diagram of the hardware structure of the electronic device provided in the embodiment of the present application. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0059] It should be noted that, although the functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0061] First, some nouns involved in this application are analyzed:

[0062] Gene sets: refers to a collection of genes associated with a specific biological process, function or pathway. These gene sets can be defined and collected through a variety of methods and databases, such as gene expression data, gene function annotation, literature mining, etc.

[0063] Biomarker: refers to a biochemical indicator / molecule that can mark changes or possible changes in the structure or function of a system, organ, tissue, cell or subcellular structure, and has a very wide range of uses. Biomarkers can be used to diagnose diseases, determine disease stages, or evaluate the safety and effectiveness of new drugs or new therapies in target populations.

[0064] Transcriptome: It is the study of gene expression at the RNA level. The transcriptome is the sum of all RNAs that can be transcribed by a living cell, and is an important means of studying cell phenotype and function. The transcription process of synthesizing RNA using DNA as a template is the first step in gene expression and a key link in gene expression regulation. The so-called gene expression refers to the entire process of converting the genetic information carried by the gene into a discernible phenotype.

[0065] Non-coding RNA (ncRNA): refers to RNA molecules produced during the transcription process that are not involved in protein coding but have other important biological functions. Unlike protein-coding mRNA, non-coding RNA is not translated into protein but exists in cells as RNA molecules.

[0066] Messenger RNA (mRNA): An RNA molecule synthesized during the transcription process that carries the genetic information encoded in DNA and transmits this information to the cytoplasm to participate in the process of protein synthesis.

[0067] Non-coding RNA sets (ncRNA sets): refers to the combination of multiple non-coding RNAs present in cells. Non-coding RNAs can be classified according to their length and function. Different types of non-coding RNAs have different regulatory effects and biological functions.

[0068] Variant sets: refers to a set of genomic variations found in a specific population. These variations can be in the form of single nucleotide polymorphisms (SNPs), insertions / deletions (Indels), structural variations (SVs), or gene rearrangements.

[0069] Ischemic Stroke (IS): It is a brain damage disease caused by insufficient blood supply to the brain. Ischemic stroke refers to a situation in which cerebral blood vessel blockage causes ischemia and hypoxia in the brain area, resulting in damage or death of brain cells. According to the TOAST etiology classification, ischemic stroke can be divided into 5 types, including: large-artery atherosclerosis, cardioembolism, small-vessel occlusion, acute stroke of other determined etiology, and stroke of undetermined etiology.

[0070] As mentioned above, complex diseases are usually caused by the interaction of multiple genes and environmental factors, and their pathogenesis is relatively complex. Therefore, it is a challenging task to screen out specific core markers to diagnose complex diseases. Traditionally, the core marker screening for complex diseases is to conduct genomic analysis on a large population, find genetic variants associated with disease risk, and use genetic variants as core markers for complex diseases. However, large-scale population genetic analysis requires the identification of a large number of genes, resulting in an excessively high number of markers identified, and it is impossible to accurately determine whether it is related to complex diseases, making the construction of core markers for complex diseases complex and low in accuracy.

[0071] Based on this, the present application embodiment provides a method for constructing a core marker specific to a complex disease function and related equipment, which is intended to obtain biological knowledge data related to the target disease, and extract the candidate genes, target function set data, disease-related marker set and disease transcriptomics data required for core marker construction from the biological knowledge data, and then analyze the correlation between each candidate gene and the target disease according to the target function set data, the disease-related marker set and the disease transcriptomics data to obtain disease correlation measurement data. The selected gene is screened out from the candidate gene through the disease correlation measurement data, so as to use the selected gene as the core marker of the specific function of the target disease. Therefore, by making full use of background knowledge such as public data and literature, and then completing the construction of the core marker of the specific function of the target disease according to the background knowledge, not only the cost of the construction of the disease core marker is reduced, but also there is no need to build a large-scale marker in advance, saving the time and computing resources for the construction of the core marker of the disease function.

[0072] The method for constructing core markers specific for complex disease functions and related equipment provided in the embodiments of the present application are specifically illustrated by the following examples. First, the method for constructing core markers specific for complex disease functions in the embodiments of the present application is described.

[0073] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0074] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0075] The method for constructing complex disease function-specific core markers provided in the embodiments of the present application relates to the field of biological detection technology. The method for constructing complex disease function-specific core markers provided in the embodiments of the present application can be applied to a terminal, can be applied to a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or can be configured as a server cluster or a distributed system composed of multiple physical servers, or can be configured as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements a method for constructing complex disease function-specific core markers, etc., but is not limited to the above forms.

[0076] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments, in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0077] It should be noted that in each specific implementation of the present application, when it comes to the need to perform relevant processing based on data related to user identity or characteristics such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0078] Figure 1 This is an optional flow chart of the method for constructing a complex disease function-specific core marker provided in the embodiments of the present application. Figure 1 The method may include but is not limited to steps S101 to S105.

[0079] Step S101, obtaining biological knowledge data related to the target disease;

[0080] Step S102, extracting candidate genes associated with the target disease, target function set data, disease-associated marker set and disease transcriptomics data from the biological knowledge data; wherein the target function set data is function set data consisting of function names associated with the target disease, RNA targeting relationships and gene genetic variation results;

[0081] Step S103, performing disease association measurement on the candidate genes according to the target function set data, the disease association marker set and the disease transcriptomics data to obtain disease association measurement data of each candidate gene;

[0082] Step S104, screening candidate genes according to the disease association measurement data to obtain selected genes;

[0083] Step S105, marking the target disease according to the selected gene to obtain a specific functional core marker of the target disease.

[0084] Steps S101 to S105 shown in the embodiment of the present application are obtained by obtaining biological knowledge data of the target disease, extracting candidate genes from the biological knowledge data, and evaluating the target function set data, disease-related marker set and disease transcriptomics data of the candidate genes and the target disease. According to the target function set data, disease-related marker set and disease transcriptomics data, the correlation between the candidate genes and the target disease is evaluated to obtain disease correlation measurement data, and the selected genes are screened out from the candidate genes through the disease correlation measurement data, and the target disease is marked according to the selected genes to obtain the specific function core markers of the target disease. Therefore, by making full use of the existing biological knowledge data that is public and related to the target disease, the correlation between the candidate genes and the target disease is analyzed according to the data that can evaluate the candidate genes in the biological knowledge data, and the genes closely related to the target disease are screened out as core markers, without the need to perform gene analysis on a large number of people, saving the cost of constructing the disease core markers, and the entire core marker construction is simple and fast, saving time and computing resources.

[0085] In step S101 of some embodiments, biological knowledge data can be obtained from public literature. Biological knowledge can also be obtained by other means, not limited to this. It should be noted that the biological knowledge data is the known biological background knowledge related to the target disease, and different biological background knowledge comes from different platforms. It is necessary to establish communication with the public platform before retrieving the biological background knowledge related to the target disease. By using the ready-made and public biological background knowledge to construct core markers for the target disease, there is no need to perform gene identification on a large population in advance, and there is no need to identify a large number of markers, which saves the cost and efficiency of constructing core markers for complex diseases.

[0086] It should be noted that the platforms for acquiring biological background knowledge in this embodiment include NCBI PubMed, Google Scholar, biological knowledge databases, literature, etc., and there is no restriction on the platforms for biological background knowledge. It should be noted that the biological knowledge database includes: biological function database, disease marker database, RNA knowledge database and large-scale population gene database, and there is no specific restriction on the biological knowledge database. Specifically, NCBI PubMed is a public biomedical literature database that covers all aspects of the biological field, including basic science, clinical medicine, epidemiology, etc. Google Scholar is a free learning search engine that includes learning papers, dissertations, research reports and conference papers from various fields. Therefore, a connection is established with a platform that is public and has knowledge of all aspects of the biological field to obtain biological knowledge data, so the biological knowledge data obtained can be accurately constructed into core markers specific to complex disease functions.

[0087] In step S102 of some embodiments, the biological knowledge data covers the biological background knowledge associated with the target disease, and comes from multiple platforms, and different platforms can extract different biological knowledge data. Therefore, candidate genes, target function sets, disease-related marker sets and disease transcriptomics data are extracted from the biological knowledge data, and the candidate genes, target function sets, disease-related marker sets and disease transcriptomics data come from different platforms. It should be noted that the candidate gene is a gene recorded in the biological background knowledge that is associated with the target disease, and the target function set data records the genes that are related to a certain function, RNA relationship, and genetic variation of the target disease. The target function set data can be used to determine which functions, RNA targeting relationships, and genetic variation degrees of the candidate genes are involved in each candidate gene. The disease-related marker set includes representative genes associated with the target disease in the biological background knowledge. Disease transcriptomics data is the sum of all RNAs that can be transcribed by a living cell, which is used to study the expression of each candidate gene. Therefore, by extracting target function set data, disease-associated marker sets, and disease transcriptomics data from biological background knowledge, specific functional core markers that characterize the target disease can be screened more accurately.

[0088] Specifically, the target function set data includes a functional gene set, a targeted gene set, and a function loss intolerance set. The functional gene set contains genes with target disease-related functions, that is, genes carrying target disease-related functions. The targeted gene set contains RNA related to the target disease and genes with a targeting relationship with the RNA. The function loss intolerance set records the function loss intolerance probability of each candidate gene, and the function loss intolerance probability represents the probability of gene function loss during the treatment of the target disease, and the function loss intolerance probability is related to factors such as abnormal mutations, environmental exposure, and drug dosage. Therefore, by screening candidate genes as specific functional core markers of the target disease from multiple aspects, the screened core markers are more accurate as research objects for the treatment of the target disease, achieving targeted disease treatment and improving the effect of disease treatment.

[0089] In some embodiments, the functional gene set is obtained from a biological function database, and the biological function database may be GO or KEGG, and there is no restriction on the category of the biological function database. It should be noted that the biological function databases named GO and KEGG are two commonly used biological function libraries, which are used to annotate and classify the functions of genes. Therefore, the functional gene set extracted from the biological function databases named GO and KEGG can characterize the corresponding genes under each function, and the same gene can have multiple functions. It should be further explained that the functional gene set is such as a GO node or a KEGG pathway, and the functional gene set is a set composed of gene lists, and a gene list records genes with the same function. There is no structural relationship between the gene lists within the GO node, while there is a structural relationship between the internal gene lists of KEGG. Therefore, the functional set data directly extracted from the biological function databases of GO and KEGG is F={f1,…,f n}, where the union of all candidate genes is G, and by i The structures and interactions contained in the gene were removed, and the functional gene set containing only gene names was obtained as In the functional gene set, individual genes may involve multiple functions or even cover all functions, so the functional gene set is divided into the core functional gene set and the specific functional gene set. The genes included in the core functional gene set are genes that appear in all functional sets, while the specific functional gene set is genes that have functions other than the genes in the core functional gene set. Therefore, the core functional gene set is denoted as G core , that is The specific functional gene set is denoted as G specific , that is And G = G core ∪G specific .

[0090] For example, if the target disease is ischemic stroke, and its abbreviation is IS, literature search based on NCBI pubmed, googlescholar and other databases, the functional nodes related to IS disease are the node GO:0034599 (cellular response to oxidative stress) in the Gene Ontology database, and the pathway hsa04080 (Neuroactive ligand-receptor interaction-Homo sapiens (human)) in the KEGG database, that is, F = {GO:0034599, hsa04080}. Among them, the union of all genes included is denoted as G, which contains 459 genes in total, of which the core functional gene set G core , including 2 genes, the specific functional gene set is denoted as G specific, containing 457 genes.

[0091] In some embodiments, the targeted gene set includes an RNA targeted gene set and an RNA relationship set. It should be noted that a list of non-coding RNAs (miRNAs, LncRNAs) related to target diseases is extracted from literature and RNA knowledge databases, and is denoted as MI = {mi1, ..., mi n}, and LN={ln1,…,ln n}, and the RNA knowledge database can be HMDD, miR2Disease, LncRNADisease v3.0 and other databases, and there is no restriction on the RNA knowledge database. Specifically, HMDD is a database that specializes in collecting and organizing disease information related to human microRNAs, miR2Disease is a database that collects information related to miRNAs and human diseases, and LncRNADisease is a database that collects information related to long non-coding RNAs (LncRNAs) and human diseases. Therefore, a list of non-coding RNAs related to the target disease is extracted through databases such as HMDD, miR2Disease, and LncRNADisease v3.0, and then the relationship set between miRNA and targeted genes is obtained from databases such as miRTarBase and LncRNA2Target based on the list of non-coding RNAs (miRNA, LncRNA) to obtain the RNA targeted gene set. Because there are two types of non-coding RNAs, the RNA targeted gene set is recorded as as well as It should be noted that miRTarBase is a database that collects information on the relationship between miRNA and targeted genes, and LncRNA2Target is a database that collects information on the relationship between LncRNA and targeted genes. Therefore, from miRTarBase and LncRNA2Target, we can directly obtain genes with targeted relationships between miRNA and LncRNA related to target diseases to construct RNA targeted gene sets. At the same time, LncBase is a database that collects information on the relationship between messenger RNA and targeted genes. Therefore, based on LncBase, we can obtain the relationship between miRNA and LncRNA to obtain the RNA relationship set, and for any miRNA mi i , the LncRNA set regulated by it is recorded as

[0092] In some embodiments, the loss-of-function intolerance degree set is obtained by acquiring genetic variation data from large-scale population genome databases such as gnomAD, dbSNP, and 1KGP. It should be noted that gnomAD is a whole-genome variation frequency database, dbSNP is a public database that maintains single nucleotide variation and structural variation information of humans and other organisms, and 1KGP is a genome sequencing project that builds a detailed human genome variation map containing diverse populations. Therefore, genetic variation data is extracted through these three databases related to gene variation, and then the loss-of-function intolerance probability of each candidate gene is calculated based on the genetic variation data to obtain the loss-of-function intolerance degree set, so the loss-of-function intolerance degree set can more accurately select the specific functional core markers of the target disease.

[0093] In some embodiments, the disease-associated marker set is a list consisting of disease markers, and biomarkers related to the target disease are extracted from the literature or disease marker database as disease-associated markers, which are recorded as Golden-sets. It should be noted that the disease marker database in this embodiment is MarkerDB and HPO (Human Phenotype Ontology), and there is no specific restriction on the disease marker database. MarkerDB is a gene marker database that provides information and resources related to gene markers, and HPO is used to describe standardized terms and classification systems for human phenotypes. By extracting the disease-associated marker set of the target disease from MarkerDB and HPO, it is possible to accurately characterize the existing knowledge record of markers related to the target disease, so as to assist in more accurately constructing core markers for the target disease.

[0094] For example, biological markers related to IS were obtained based on the HPO database, and a total of 37 genes were obtained to obtain a disease-associated marker set.

[0095] In some embodiments, the disease transcriptomics data is a data set composed of transcriptomics data of the target disease, and the disease transcriptomics data is obtained based on databases such as NCBI GEO and EBI ArrayExpress, and there is no restriction on the database from which the disease transcriptomics data is obtained. It should be noted that NCBI GEO and EBI ArrayExpress both maintain and manage a public gene expression data repository. Therefore, the expression data of each candidate gene can be obtained through NCBI GEO and EBI ArrayExpress, so that the correlation between the candidate gene and the target disease can be analyzed more accurately, so as to select the core markers of specific functions that can characterize the target disease.

[0096] In summary, by selecting different databases or literature for data extraction based on different data, we can make full use of the existing public biological background knowledge to assist in the construction of specific functional core markers for the target disease, without the need to perform genetic analysis and various experiments on the target disease, thus saving the cost of constructing specific functional core markers for the target disease.

[0097] See also Figure 2 In some embodiments, step S103 may include but is not limited to steps S201 to S206:

[0098] Step S201, measuring the pathogenicity of each candidate gene according to the disease-associated marker set to obtain pathogenicity measurement data;

[0099] Step S202, measuring the functional contribution of each candidate gene according to the functional gene set to obtain functional contribution measurement data;

[0100] Step S203, performing differential expression measurement on each candidate gene according to the disease transcriptomics data to obtain differential expression measurement data;

[0101] Step S204, performing gene targeting measurement on each candidate gene according to the targeted gene set to obtain gene targeting measurement data;

[0102] Step S205, performing a function loss intolerance measurement on each candidate gene according to the function loss intolerance degree set to obtain function loss intolerance measurement data;

[0103] Step S206, constructing the disease association metric data of each candidate gene from the pathogenicity metric data, the functional contribution metric data, the differential expression metric data, the gene targeting metric data and the function loss intolerance metric data.

[0104] In step S201 of some embodiments, the disease-associated marker set records the genes that cause the target disease, so the disease-associated marker set is used to determine whether the candidate gene is included in the disease-associated marker set to determine the pathogenicity of the candidate gene to obtain pathogenicity measurement data. It should be noted that the pathogenicity measurement data is used to measure the pathogenicity of each candidate gene, that is, to analyze whether the candidate disease is the pathogenic gene of the target disease.

[0105] Specifically, the pathogenicity measurement of the candidate gene is shown in formula (1):

[0106]

[0107] In the formula, g j is any candidate gene in G, and Golden-sets is a set of disease-associated markers.

[0108] For example, if the IS-related disease-associated marker set has 37 genes and G exists in 13 genes, then the pathogenicity measurement data of these 13 candidate genes is 1, and the pathogenicity measurement data of the remaining genes is 0.

[0109] In step S202 of some embodiments, the functional gene set is composed of multiple functional gene lists, and each functional gene list contains genes with the same function. The contribution measurement data characterizes the contribution of the candidate gene to the target disease-related functions, and the functional contribution measurement of the candidate gene is also to determine the number of functions possessed by each candidate gene. The more functions the candidate gene possesses, the higher the functional contribution measurement data, indicating that the candidate gene has an impact on multiple specific functions in the target disease.

[0110] Specifically, the functional contribution measurement of the candidate genes is shown in formula (2):

[0111]

[0112]

[0113] Among them, 1≤i≤n, The value range of is (0, 1]. As can be seen from the above, by calculating the number of functions of each candidate gene, and then using the ratio between the number of functions and the total number of functions as the functional contribution measurement data, the functional contribution of each candidate gene to the target disease can be accurately characterized.

[0114] In step S203 of some embodiments, the disease transcriptomics data records the gene expression of each candidate gene, and the differential expression metric data characterizes the gene expression of the candidate gene. If the gene is significantly expressed in the target disease, the gene has a greater impact on the target disease. Therefore, by differentially metricizing the candidate genes through the disease transcriptomics data, the expression of each candidate gene for the target disease can be obtained, and the candidate genes selected based on the differential expression metric data can be used as the core markers of the specific functions of the target disease.

[0115] In step S204 of some embodiments, the targeted gene set records the targeted genes of the target disease-related RNA, and the gene targeting metric data characterizes the targeting of the candidate genes to the target disease-related RNA. Then, by screening the candidate genes based on the gene targeting metric data, genes that are more related to the target disease can be selected as core markers.

[0116] In step S205 of some embodiments, the function loss intolerance measurement data indicates that the gene has a function loss mutation, and these mutations cause the gene or gene product to be unable to perform its biological function normally. If the gene loses its original function, it may be the gene that causes the target disease, so the gene selected according to the function loss intolerance measurement data is more accurate as the core marker of the specific function of the target disease.

[0117] In steps S201 to S206 shown in this embodiment, pathogenicity measurement, functional contribution measurement, differential expression measurement, gene targeting measurement and function loss intolerance measurement are performed on the candidate genes to measure whether the candidate genes have an impact on the target disease from five aspects, so as to screen out specific functional core markers that can accurately characterize the target disease.

[0118] See also Figure 3 In some embodiments, step S203 may include but is not limited to steps S301 to S304:

[0119] Step S301, obtaining data categories of disease transcriptomics data;

[0120] Step S302, preprocessing the disease transcriptomics data according to the data category to obtain candidate transcriptomics data;

[0121] Step S303, performing matrix conversion on the candidate transcriptomics data to obtain a transcriptomics matrix; wherein each row in the transcriptomics matrix represents a gene, and each column represents a sample;

[0122] Step S304, performing differential expression measurement on the transcriptomics matrix to obtain differential expression measurement data.

[0123] In steps S301 to S302 of some embodiments, the data categories of disease transcriptomics data include gene chips, deep sequencing data, and single-cell sequencing data, etc., and the disease transcriptomics data are preprocessed differently according to different data categories, so as to achieve targeted preprocessing of disease transcriptomics data, so as to convert disease transcriptomics data of different data categories into data in a unified format. It should be noted that, for the convenience of transcriptomics data, they are uniformly characterized as behavioral genes, listed as samples, and the expression values ​​are in the range of 0 to 1.

[0124] Specifically, if the disease transcriptomics data is gene chip data, it is necessary to perform probe matching, multi-probe processing, missing value filling and sample screening on the gene chip data, and the pre-processing operation for the gene chip data is not limited thereto. If the disease transcriptomics data is deep sequencing data (bulk RNA-seq data), low-quality read filtering, removal of joints, removal of short sequences, and qualitative and quantitative analysis of RNA are realized using preset tools. It should be noted that the preset tools are fastp, Hisat2, and featurecounts in this embodiment, and other embodiments are not limited thereto. Specifically, fastp, Hisat2, and featurecounts are used to process common sequencing data, and corresponding pre-processing operations can be performed on sequencing data. If the disease transcriptomics data is single-cell sequencing data (scRNA-seq / snRNA-seq data), tools such as seurat are used to perform quality control, filtering, and batch effect correction on single-cell sequencing data, and seurat is software for single-cell transcriptomics data (e.g., software package of R language), single-cell sequencing data can be efficiently pre-processed, and the present embodiment does not specifically limit the selected tools.

[0125] In step S303 of some embodiments, the preprocessed candidate transcriptomics data is converted into an expression spectrum in matrix form to obtain a transcriptomics matrix, and each row in the transcriptomics matrix represents the expression value of a candidate gene, a column represents a sample, and the range of the expression value is [0, 1].

[0126] In step S304 of some embodiments, because the matrix records the expression value of each candidate gene in each sample, the expression difference of each candidate gene in the disease group and the normal group can be calculated according to the transcriptomics matrix to obtain differential expression metric data, making the calculation of differential expression metric data easier and more accurate.

[0127] In steps S301 to S304 shown in this embodiment, disease transcriptomics data of different data categories are preprocessed, and then the preprocessed transcriptomics data are converted into a transcriptomics matrix in matrix form, so that the differential expression of each candidate gene can be measured more easily and accurately through the transcriptomics matrix.

[0128] See also Figure 4 In some embodiments, step S304 may include but is not limited to steps S401 to S405:

[0129] Step S401, extracting the disease group expression value and the normal group expression value from the transcriptomics matrix;

[0130] Step S402, obtaining the differential expression value between the disease group expression value and the normal group expression value;

[0131] Step S403, merging the differential expression values ​​to obtain a target expression value;

[0132] Step S404, determining the level data according to the target expression value and the preset expression threshold;

[0133] Step S405, performing mean processing on the level data to obtain differential expression measurement data.

[0134] In step S401 of some embodiments, the transcriptomic matrix records the expression values ​​of the disease group and the expression values ​​of the normal group, and there are multiple transcriptomic matrices for each candidate gene. It is necessary to calculate the expression value of the disease group in each transcriptomic matrix as the disease group expression value, and then calculate the expression value of the normal group in each transcriptomic matrix as the normal group expression value. It should be noted that the disease group expression value is recorded as The normal expression group was recorded as

[0135] In step S402 of some embodiments, the differential expression value represents the expression difference of the candidate gene between the disease group and the normal group, and in this embodiment, it is necessary to make a ratio between the disease group expression value and the normal group expression value and then take the logarithm as the differential expression value, and the differential expression value representation is shown in formula (3):

[0136]

[0137] Where x represents any set of data in the k transcriptomics matrices.

[0138] In step S403 of some embodiments, for the same candidate gene, the differential expression values ​​obtained from different data are recorded as By differential expression The target expression value is obtained by merging the differential expression values. It should be noted that in this embodiment, the differential expression values ​​are combined and calculated by methods such as Fisher's combined probability test.

[0139] Specifically, the target expression value is calculated as shown in formula (4):

[0140]

[0141] In step S404 of some embodiments, the target expression value is compared with a preset expression threshold to obtain level data, and the level data is defined as Specifically, the level data represents the gene expression level of the candidate gene. If the target expression value is less than or equal to the expression threshold, the level data is determined to be 1; if the target expression value is greater than the expression threshold, the level data is determined to be 0.

[0142] The determination of grade data is shown in formula (5):

[0143]

[0144] In step S405 of some embodiments, the level data of each transcriptome matrix is The differential expression metric data is obtained by calculating the mean of the level data corresponding to the K transcriptomics matrices, and the differential expression metric data is shown in formula (6):

[0145]

[0146] In the formula, is the differential expression metric data, and k is the transcriptome matrix.

[0147] For example, two sets of data from the NCBI GEO database containing AIS (acute ischemic stroke) and healthy controls: GSE16561 (gene chip data) and GSE202518 (RNA-seq data) were selected for differential expression analysis. The threshold of the value is 0.05. After calculation, after merging The minimum value is 0.05843998, so the differential expression metric data are all assigned a value of 0.

[0148] In steps S401 to S405 shown in this embodiment, the differential expression values ​​between the disease group expression values ​​and the normal group expression values ​​are obtained, and then merged into target expression values ​​according to the differential values. Finally, the level data corresponding to each sample column is calculated according to the target expression value and the preset expression threshold, and the mean of the level data is used as the differential expression measurement data, so that the differential expression measurement data of each candidate gene is easy to calculate and can accurately characterize the gene expression of each candidate gene.

[0149] See also Figure 5 In some embodiments, step S204 may include but is not limited to steps S501 to S504:

[0150] Step S501, performing targeted measurement on the candidate gene according to the miRNA targeted gene set to obtain first targeted measurement data;

[0151] Step S502, performing targeted measurement on the candidate gene according to the LncRNA targeted gene set to obtain second targeted measurement data;

[0152] Step S503, performing targeting relationship measurement on the candidate genes according to the miRNA targeting gene set, the LncRNA targeting gene set and the RNA relationship set to obtain targeting relationship measurement data;

[0153] Step S504: concatenate the first targeting metric data, the second targeting metric data and the targeting relationship metric data to obtain gene targeting metric data.

[0154] In step S501 of some embodiments, the first targeting metric data characterizes the targeting between the candidate gene and the miRNA, that is, it is determined whether there is a targeting relationship between the candidate gene and the miRNA related to the target disease. Specifically, the number of candidate genes in the miRNA targeted gene set is determined as the number of miRNA targeted regulation, and then the ratio between the number of miRNA targeted regulation and the total number of candidate genes is obtained as the first targeting metric data. Specifically, the first targeting metric data is calculated as shown in formula (7):

[0155]

[0156]

[0157] Among them, 1≤i≤n, First targeted metric data The value range of is [0, 1].

[0158] In step S502 of some embodiments, the second targeting metric data characterizes the targeting between the candidate gene and the LncRNA, that is, it is determined whether there is a targeting relationship between the candidate gene and the LncRNA related to the target disease. Specifically, the number of candidate genes in the LncRNA targeted gene set is determined as the LncRNA targeted regulation number, and then the ratio between the Lnc targeted regulation number and the total number of candidate genes is obtained as the second targeting metric data. Specifically, the second targeting metric data is calculated as shown in formula (8):

[0159]

[0160]

[0161] Among them, 1≤i≤n, Second Targeted Metrics Data The value range of is (0, 1].

[0162] In step S503 of some embodiments, after completing the measurement of the targeting relationship between the candidate gene and the miRNA and LncRNA, it is necessary to determine whether there is a competitive relationship between the miRNA targeted gene set and the LncRNA targeted gene set. It is necessary to determine whether the candidate gene appears in the miRNA targeted gene set and the LncRNA targeted gene set, and it is also necessary to determine whether there is a competitive relationship between the miRNA targeted gene set and the LncRNA targeted gene set to obtain the targeting relationship measurement data, and the targeting relationship measurement data is shown in formula (9):

[0163]

[0164] In the formula, For the miRNA target gene set, It is a collection of LncRNA targeted genes.

[0165] In step S504 of some embodiments, the first targeting metric data, the second targeting metric data and the targeting relationship metric data are spliced ​​into gene targeting metric data, and the splicing method includes but is not limited to: summing, averaging, and weighted summing.

[0166] For example, 105 ncRNAs related to IS were downloaded from the HMDD database. MI contains 100 elements and LN contains 5 elements. i And LncRNA ln i Based on the miRTarBase, LncRNA2Target and other databases, we obtained the relationship set between miRNA and its target genes. The experimentally confirmed target genes of 46 miRNAs are 1992, that is, there are 1992 genes in the miRNA targeted gene set, while 5 LncRNAs have no experimentally confirmed target gene set. Therefore, the first targeted metric data of all genes and second targeted metric data The values ​​are all 0.

[0167] In steps S501 to S504 shown in this embodiment, by analyzing the targeting relationship between each candidate gene and the miRNA and LncRNA related to the target disease, it is determined whether there is a competitive relationship between the two targeted gene sets to construct gene targeting measurement data for each candidate gene, so as to accurately characterize the association between the candidate gene and the target disease through the gene targeting measurement data.

[0168] See also Figure 6 In some embodiments, step S205 includes but is not limited to steps S601 to S602:

[0169] Step S601, obtaining the gene name of the candidate gene;

[0170] Step S602, searching for the probability of loss-of-function intolerance in the loss-of-function intolerance degree set according to the gene name to obtain loss-of-function intolerance measurement data.

[0171] In steps S601 to S602 of some embodiments, by obtaining the gene name of each candidate gene, the corresponding function loss intolerance probability is directly found in the function loss intolerance degree set according to the gene name as function loss intolerance measurement data, which is used to determine the tolerance of the candidate gene to function loss.

[0172] Specifically, for the function loss intolerance degree set, the function loss intolerance probability is calculated as shown in formula (10):

[0173]

[0174] in, Indicates candidate gene g j The frequency of mutations in According to the candidate gene g j The size of the candidate gene, the expected distribution of mutation types, and the functional impact of the mutation are used to calculate the probability of intolerance to loss of function, with a value range of [0, 1], where 0 indicates that the candidate gene is very tolerant to loss of function and 1 indicates that the candidate gene is very intolerant to loss of function.

[0175] For example, based on the probability calculation of function loss intolerance: Get the above 459 genes from the function loss intolerance degree set In this embodiment, the functional intolerance level is set as reference [PMID: 30531870].

[0176] In step S601 to step S602 shown in this embodiment, the corresponding function loss intolerance probability is directly found from the function loss intolerance degree set according to the name of each candidate gene, so as to clarify the tolerance degree of each candidate gene to function loss.

[0177] In step S206 illustrated in some embodiments, the pathogenicity measurement data GG, the functional contribution measurement data KG, the differential expression measurement data CF, the first targeted measurement data MIG, the second targeted measurement data LNG, the targeted relationship measurement data MLG and the functional intolerance measurement data VPG of each candidate gene are combined. The pathogenicity measurement data GG, the functional contribution measurement data KG, the differential expression measurement data CF, the first targeted measurement data MIG, the second targeted measurement data LNG, the targeted relationship measurement data MLG and the functional intolerance measurement data VPG are combined into disease-related measurement data by using the mean or cumulative method. It should be noted that there is no specific limitation on the method of combining multiple data into disease-related measurement data.

[0178] See also Figure 7 In some embodiments, step S104 may include but is not limited to steps S701 to S702:

[0179] Step S701, sorting the candidate genes in descending order according to the disease association measurement data to obtain a sorting number for each candidate gene;

[0180] Step S702: extracting selected genes from candidate genes according to a preset gene selection quantity and sorting sequence number.

[0181] In step S701 of some embodiments, the candidate genes are sorted in descending order according to the disease association measurement data to obtain a ranking number for each candidate gene, that is, the candidate genes are sorted in descending order according to the disease association measurement data. The ranking number represents the role played by the candidate gene in the occurrence and development of the target disease.

[0182] In step S702 of some embodiments, a preset number of candidate genes before the sorting sequence is obtained as selected genes to select genes that can represent the target disease.

[0183] For example, if there are 459 genes related to IS disease, the pathogenicity measurement data GG, functional contribution measurement data KG, differential expression measurement data CF, first targeted measurement data MIG, second targeted measurement data LNG, targeted relationship measurement data MLG and functional loss intolerance measurement data VPG are selected in an accumulation manner to splice the disease association measurement data T_score. Arrange T_score in descending order to obtain the final score of the relationship between genes and diseases. Set x = 5, that is, select the 23 genes ranked in the top 5% of T_score as the specific functional core markers of IS, and the final result is shown in Table 1 below:

[0184]

[0185]

[0186] Table 1

[0187] In steps S701 to S702 shown in this embodiment, by selecting several candidate genes with the highest correlation as selected genes, genes that play an important role in the occurrence and development of the target disease are selected, so that the construction of the specific functional core markers of the target disease is more accurate.

[0188] Please refer to Figure 8 In this embodiment, the functional gene set is extracted from the biological function database named GO and KEGG. Extract disease-associated marker sets of target diseases from MarkerDB and HPO, denoted as "Golden-sets". Obtain disease transcriptomics data based on databases such as NCBI GEO and EBI ArrayExpress, denoted as omics-sets. Extract a list of non-coding RNAs related to target diseases through databases such as HMDD, miR2Disease, and LncRNADisease v3.0, and then obtain a set of relationships between miRNAs and targeted genes from databases such as miRTarBase and LncRNA2Target based on the list of non-coding RNAs (miRNAs, LncRNAs) to obtain a set of RNA targeted genes, denoted as as well as Based on LncBase, the relationship between miRNA and LncRNA is obtained to obtain the RNA relationship set, and for any miRNA mi i , the LncRNA set regulated by it is recorded as Genetic variation data are obtained from large-scale population genome databases such as gnomAD, dbSNP, and 1KGP to obtain the function loss intolerance degree set V = {v1, ..., v n}. The pathogenicity measurement data GG is determined by judging whether the candidate gene is in the disease-associated marker set. The number of functions of each candidate gene is calculated, and then the ratio between the number of functions and the total number of functions is used as the functional contribution measurement data KG. The disease transcriptomics data of different data categories are preprocessed, and then the preprocessed transcriptomics data are converted into a transcriptomics matrix in matrix form. The differential expression values ​​between the disease group expression value and the normal group expression value are obtained from the transcriptomics matrix, and then the target expression values ​​are merged according to the differential values. Finally, the level data corresponding to each sample column is calculated according to the target expression value and the preset expression threshold, and the mean of the level data is used as the differential expression measurement data CF. The number of candidate genes in the miRNA targeted gene set is determined as the number of miRNA targeted regulation, and then the ratio between the number of miRNA targeted regulation and the total number of candidate genes is obtained as the first targeted measurement data MIG, the number of candidate genes in the LncRNA targeted gene set is determined as the number of LncRNA targeted regulation, and then the ratio between the number of Lnc targeted regulation and the total number of candidate genes is obtained as the second targeted measurement data LNG, and the competitive relationship between the miRNA targeted gene set and the LncRNA targeted gene set is determined to obtain the targeted relationship measurement data MLG. According to the name of each candidate gene, the corresponding function loss intolerance probability is found from the function loss intolerance degree set to obtain the function loss intolerance measurement data VPG. The pathogenicity measurement data GG, the functional contribution measurement data KG, the differential expression measurement data CF, the first targeted measurement data MIG, the second targeted measurement data LNG, the targeted relationship measurement data MLG and the function loss intolerance measurement data VPG are combined into disease association measurement data by using the mean or cumulative method. The candidate genes are sorted in descending order according to the disease association measurement data to obtain the sorting number of each candidate gene, and the candidate genes with a preset number of gene selections before the sorting number are obtained as selected genes, and the function of the target disease is marked by the selected genes to obtain the specific functional core marker.

[0189] See also Fig. 9 The present application also provides a device for constructing a complex disease function-specific core marker, which can implement the above-mentioned complex disease function-specific core marker construction method, and the device includes:

[0190] The data acquisition module 901 is used to acquire biological knowledge data related to the target disease;

[0191] Extraction module 902, used to extract candidate genes associated with target diseases, target function set data, disease-associated marker sets and disease transcriptomics data from biological knowledge data; wherein the target function set data is function set data composed of function names associated with target diseases, RNA targeting relationships and gene genetic variation results;

[0192] The association measurement module 903 is used to perform disease association measurement on the candidate genes according to the target function set data, the disease association marker set and the disease transcriptomics data to obtain disease association measurement data of each candidate gene;

[0193] A screening module 904 is used to screen candidate genes according to the disease association measurement data to obtain selected genes;

[0194] The labeling module 905 is used to label the target disease according to the selected gene to obtain the specific functional core marker of the target disease.

[0195] The specific implementation of the complex disease function-specific core marker construction device is basically the same as the specific embodiment of the complex disease function-specific core marker construction method described above, and will not be repeated here.

[0196] The present application also provides an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the method for constructing the core markers specific to the function of the complex disease when executing the computer program. The electronic device can be any intelligent terminal including a tablet computer, a car computer, etc.

[0197] See also Fig.10 , Fig.10 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0198] The processor 1001 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0199] The memory 1002 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 1002 can store an operating system and other applications. When the technical solution provided in the embodiments of this specification is implemented by software or firmware, the relevant program code is stored in the memory 1002, and the processor 1001 calls and executes the method for constructing core markers specific to complex disease functions in the embodiments of this application;

[0200] Input / output interface 1003, used to implement information input and output;

[0201] The communication interface 1004 is used to realize the communication interaction between the device and other devices. The communication can be realized through a wired manner (such as USB, network cable, etc.) or a wireless manner (such as mobile network, WIFI, Bluetooth, etc.);

[0202] A bus 1005 , which transmits information between various components of the device (e.g., the processor 1001 , the memory 1002 , the input / output interface 1003 , and the communication interface 1004 );

[0203] The processor 1001 , the memory 1002 , the input / output interface 1003 and the communication interface 1004 are connected to each other in communication within the device via the bus 1005 .

[0204] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method for constructing the core markers specific to the functions of the above-mentioned complex diseases.

[0205] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0206] The method for constructing complex disease function-specific core markers and related equipment provided in the embodiments of the present application, which extracts biological knowledge data related to the target disease from an existing biological database, and the biological knowledge data extracted from different biological databases include candidate genes associated with the target disease, target function set data, disease-related marker sets and disease transcriptomics data, and analyzes the correlation between each candidate gene and the target disease through the target function set data, disease-related marker sets and disease transcriptomics data to obtain disease correlation measurement data, so as to screen out selected genes that play an important role in the occurrence and development of the target disease from the candidate genes through the disease correlation measurement data, so as to use the selected genes as the core markers of the function of the target disease. Therefore, the core markers specific to the function of the target disease are constructed through the existing public background knowledge, and the target function set data, disease-related marker sets and disease transcriptomics data in the public background knowledge are used to analyze the correlation between genes and diseases, so that the core markers specific to the function of the target disease are constructed cost-effectively and accurately.

[0207] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0208] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0209] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0210] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0211] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0212] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0213] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0214] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0215] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0216] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0217] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but the scope of the rights of the present invention is not limited thereto. Any modification, equivalent substitution and improvement made by a person skilled in the art without departing from the scope and essence of the present invention should be within the scope of the rights of the present invention.

Claims

1. A method for constructing a core marker specific for complex disease functions, characterized in that: The method comprises: Acquire biological knowledge data related to target diseases; Extracting candidate genes, target function set data, disease-associated marker sets and disease transcriptomics data associated with the target disease from the biological knowledge data; wherein the target function set data is function set data consisting of function names, RNA targeting relationships and gene genetic variation results associated with the target disease; Performing disease association measurement on the candidate genes according to the target function set data, the disease-associated marker set and the disease transcriptomics data to obtain disease association measurement data of each candidate gene; Screening the candidate genes according to the disease association measurement data to obtain selected genes; The target disease is marked according to the selected gene to obtain a core marker of the target disease.

2. The method according to claim 1, characterized in that The target function set data includes: a functional gene set, a targeted gene set and a function loss intolerance degree set, wherein the functional gene set represents a gene set with functions related to the target disease, the targeted gene set represents a gene set with a targeting relationship with the RNA of the target disease, and the function loss intolerance degree set includes the function loss intolerance degree of each candidate gene; the disease association degree of the candidate gene is evaluated according to the target function set data, the disease association marker set and the disease transcriptomics data to obtain the disease association measurement data of each candidate gene, including: Performing pathogenicity measurement on each of the candidate genes according to the disease-associated marker set to obtain pathogenicity measurement data; Performing functional contribution measurement on each candidate gene according to the functional gene set to obtain functional contribution measurement data; Perform differential expression measurement on each of the candidate genes according to the disease transcriptomics data to obtain differential expression measurement data; Performing gene targeting measurement on each of the candidate genes according to the targeted gene set to obtain gene targeting measurement data; Performing a function loss intolerance measurement on each of the candidate genes according to the function loss intolerance degree set to obtain function loss intolerance measurement data; The pathogenicity metric data, the functional contribution metric data, the differential expression metric data, the gene targeting metric data and the loss-of-function intolerance metric data are constructed into the disease association metric data of each candidate gene.

3. The method according to claim 2, characterized in that The step of performing differential expression measurement on each of the candidate genes according to the disease transcriptomics data to obtain differential expression measurement data includes: Acquire the data category of the disease transcriptomics data; Preprocessing the disease transcriptomics data according to the data category to obtain candidate transcriptomics data; Performing matrix conversion on the candidate transcriptomics data to obtain a transcriptomics matrix; wherein each row in the transcriptomics matrix represents a gene, and each column represents a sample; The transcriptomic matrix is ​​subjected to differential expression measurement to obtain the differential expression measurement data.

4. The method according to claim 3, characterized in that The step of performing differential expression measurement on the transcriptomics matrix to obtain the differential expression measurement data includes: Extracting disease group expression values ​​and normal group expression values ​​from the transcriptomic matrix; Obtaining a differential expression value between the disease group expression value and the normal group expression value; The differential expression values ​​are combined to obtain a target expression value; Determining grade data according to the target expression value and a preset expression threshold; The level data are processed by mean value to obtain the differential expression measurement data.

5. The method according to claim 2, characterized in that: The targeted gene set includes: a miRNA targeted gene set, a LncRNA targeted gene set and an RNA relationship set; performing gene targeting evaluation on each candidate gene according to the targeted gene set to obtain gene targeting metric data includes: Performing a targeting measurement on the candidate gene according to the miRNA targeted gene set to obtain first targeting measurement data; Performing a targeting measurement on the candidate gene according to the LncRNA targeted gene set to obtain second targeting measurement data; Performing targeting relationship measurement on the candidate genes according to the miRNA targeting gene set, the LncRNA targeting gene set and the RNA relationship set to obtain targeting relationship measurement data; The first targeting metric data, the second targeting metric data and the targeting relationship metric data are spliced ​​to obtain the gene targeting metric data.

6. The method according to claim 2, characterized in that The step of measuring the loss-of-function intolerance for each candidate gene according to the loss-of-function intolerance degree set to obtain loss-of-function intolerance measurement data includes: Obtaining the gene name of the candidate gene; A loss-of-function intolerance probability search is performed in the loss-of-function intolerance degree set according to the gene name to obtain the loss-of-function intolerance measurement data.

7. The method according to any one of claims 1 to 6, characterized in that: The screening of the candidate genes according to the disease association measurement data to obtain the selected genes includes: Sorting the candidate genes in descending order according to the disease association measurement data to obtain a ranking number for each candidate gene; The selected genes are extracted from the candidate genes according to a preset gene selection quantity and the sorting sequence number.

8. A device for constructing core markers specific for complex disease functions, characterized in that: The device comprises: A data acquisition module is used to obtain biological knowledge data related to the target disease; An extraction module, used to extract candidate genes associated with the target disease, target function set data, disease-associated marker set and disease transcriptomics data from the biological knowledge data; wherein the target function set data is function set data composed of function names associated with the target disease, RNA targeting relationships and gene genetic variation results; A correlation measurement module, used for performing disease correlation measurement on the candidate genes according to the target function set data, the disease-related marker set and the disease transcriptomics data, to obtain disease correlation measurement data of each candidate gene; A screening module, used to screen the candidate genes according to the disease association measurement data to obtain selected genes; The marking module is used to mark the target disease according to the selected gene to obtain the specific functional core marker of the target disease.

9. An electronic device, characterized in that: The electronic device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the method for constructing a complex disease function-specific core marker as claimed in any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for constructing a complex disease function-specific core marker according to any one of claims 1 to 7 is implemented.

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