55 results about "Clinical phenotype" patented technology

The invention provides an IBS micro-ecological transplantation intelligent prediction method and system based on multi-omics driving, and relates to the technical field of biomedicine. The method comprises the following steps: establishing a multi-omics data fusion subsystem to collect metagenome, metabolome, host genome and clinical phenotype group data of a target patient; inputting the data into a flora-metabolite combined network analysis model to construct an interaction network and extracting features; generating an incidence matrix based on the features and the host genome data and calculating indexes; generating indexes through a dynamic response algorithm in combination with the clinical phenotypic data and the indexes; and outputting a curative effect prediction result by using a transfer learning framework combined with modeling. The system comprises a data acquisition module, a network analysis module, a correlation calculation module, a dynamic response module and a joint modeling module. According to the method, multiple omics data are integrated, the flora and host relation is accurately mined, intelligent prediction of the micro-ecological transplantation curative effect is achieved, powerful support is provided for IBS personalized treatment, and meanwhile data processing and safety guarantee measures are taken.

The invention relates to a pathogenic gene identification method and system based on multi-agent debate and a medium. The method comprises the following steps: acquiring gene detection data and clinical phenotype data; the data agent processes the acquired data, and calls a gene variation pathogenicity analysis operator and a molecular genetic large model to obtain first pathogenic gene information; the knowledge agent calls a molecular genetic large model to obtain second pathogenic gene information based on the first pathogenic gene information; the knowledge and data agents sequentially speak and debate pathogenicity of candidate pathogenic genes in the first or second pathogenic gene information on the basis of sorting results in the pathogenic gene information output by the knowledge and data agents; after each round of debate is finished, the debate agent judges whether the sorting results of the data and the knowledge agent on the candidate pathogenic genes are consistent or not, if the sorting results are consistent or the number of debate rounds reaches a threshold value, debate is finished, the debate agent conducts reasoning and outputs a result, and if not, the next round of debate is started. Compared with the prior art, the method has the advantages of no dependence on large-scale training data, high interpretability and the like.

An analytics computing device is provided. The analytics computing device includes a processor in communication with a database. The database configured to store electronic health record (EHR) data including structured EHR data and unstructured EHR data for a patient. The processor is configured to retrieve the EHR data from the database. The processor is further configured to parse, using a natural language processing model, the unstructured EHR data to retrieve one or more indicator phrases, the one or more indicator phrases correlated to an Alzheimer's disease (AD) diagnosis. The processor is further configured to identify, using a predictive model, the patient as being at risk for AD based on the retrieved indicator phrases and on the structured EHR data.

The invention discloses a method and a system for assisting in inputting clinical information of rare diseases and analyzing gene mutation based on phenotypes. The method comprises the following steps: firstly, acquiring clinical information in voice, text and image forms of a patient through a multi-source data acquisition module, converting the clinical information into characters, and performing entity recognition and standardization processing to generate structured medical record data; secondly, extracting clinical phenotypes from the structured data; furthermore, a candidate gene list is obtained according to the gene-disease relationship, comprehensive scoring and sorting are carried out, and a concerned gene list is output. According to the method, efficient structured input and standardization of clinical information are realized, the accuracy and automation level of phenotype-gene matching are remarkably improved, the gene variation interpretation period is effectively shortened, and intelligent support is provided for precise diagnosis of genetic diseases.

The invention belongs to the technical field of biological detection, and discloses a screening method of respiratory tract infection related biomarkers based on metatranscriptomics. Respiratory tract infection samples with different clinical phenotypes are subjected to metatranscriptome sequencing, data quality control, comparison and transcript quantification, pathogen and host information is reserved, then genes which are stably expressed or remarkably changed in different groups are identified by combining differential expression analysis and co-expression analysis, and potential biomarkers are obtained. And taking an intersection gene of three machine learning algorithms including an LASSO algorithm, a random forest model and an SVM model to obtain the biomarker. The invention provides a biomarker screening method for rapid and accurate identification of respiratory tract infection.

The invention discloses construction and application of a Primrose syndrome non-human animal model, and belongs to the technical field of animal models and disease research. According to the invention, through a CRISPR / Cas9 technology, a codon CAC for coding 596th histidine in a No.14 exon of a mouse Zbtb20 gene is subjected to site-directed mutagenesis into CGC for coding arginine, and a hybrid mouse model for simulating pathogenic point mutation (p.H596R) of human Primrose syndrome is constructed. The model can stably reproduce key clinical phenotypes of the Primrose syndrome, including overgrowth after adult, serum IGF-1 rise, memory dysfunction and anxiety behaviors, and shows abnormal hippocampal neuronal development and synaptic transfer related pathways and the like. The invention provides an important experimental tool for deeply revealing the pathological mechanism of Primrose syndrome, developing drug screening and treatment intervention research and the like.

The application belongs to the technical field of biological detection, and discloses a screening method of respiratory tract infection related biomarkers based on macro-transcriptomics. The application performs macro-transcriptome sequencing on respiratory tract infection samples with different clinical phenotypes, performs data quality control, alignment, transcript quantification, retains pathogen and host information, and then identifies genes stably expressed or significantly changed in different groups by combining differential expression analysis and co-expression analysis, obtains potential biomarkers, and obtains the biomarkers by taking the intersection genes of three machine learning algorithms of LASSO algorithm, random forest model and SVM model. The application provides a screening method of biomarkers for rapid and accurate identification of respiratory tract infection.

The invention relates to the technical field of bioinformatics and medical data processing, and discloses a polygene molecular diagnosis model and a construction method and application thereof.The construction method comprises the steps that a data acquisition module constructs a genome, clinical phenotype and environmental exposure data matrix, a preprocessing module executes regression filling and standardizes continuous variables, and a data processing module performs data processing; the feature screening module executes double-layer screening by using LASSO and a random forest model to output a core feature subset, the model building module builds a logic regression architecture to calculate a baseline logarithm probability, and the dynamic updating module outputs a real-time risk probability in combination with follow-up visit environment data, a time adjustment coefficient and the baseline logarithm probability. And the interactive output module outputs a risk layering label and a feature contribution degree. According to the method, redundancy is eliminated through double-layer screening, the time dimension is introduced to adjust the real-time correction probability, visual attribution is realized in combination with the SHAP algorithm, and the dynamic monitoring capability and interpretability are improved.

The invention relates to the technical field of biomedicine, discloses application of an HGF receptor inhibitor in preparation of a medicine for treating / preventing myasthenia gravis, provides a novel treatment medicine for the myasthenia gravis, and innovatively discovers that the HGF receptor inhibitor can be used for treating / preventing the myasthenia gravis by regulating body inflammation and relieving the progress, clinical phenotype and pathological phenotype of MG diseases. The myasthenia gravis disease is treated.

The present invention provides a method, apparatus, terminal, and medium for screening gene mutation points based on clinical phenotypes. The method includes: identifying candidate genes based on gene detection results, clinical text to be analyzed, and a multi-source database; calculating the basic weights of a first pathway; extracting candidate mutation sites within the genomic regions of the candidate genes and obtaining external evidence corresponding to the candidate mutation sites; performing phenotypic extraction on the clinical text to be analyzed to obtain a structured phenotype; inputting the structured phenotype, contextual information of the candidate mutation sites, and external evidence into a trained mutation site screening model to obtain intermediate semantic analysis results; determining the basic weights of a second pathway based on the intermediate semantic analysis results; obtaining a comprehensive weight for each candidate mutation site based on the basic weights of the first and second pathways; and screening the candidate mutation sites based on the comprehensive weights to obtain screening results. This application can perform site-level screening, improving the accuracy of gene mutation point screening.

A business method for use in classifying patient samples. The method includes steps of collecting case samples representing a clinical phenotypic state and control samples representing patients without said clinical phenotypic state. Preferably the system uses a mass spectrometry platform system to identify patterns of polypeptides in said case samples and in the control samples without regard to the specific identity of at least some of said polypeptides. Based on identified representative patterns of the state, the business method provides for the marketing of diagnostic products using representative patterns. The present invention relates to systems and methods for identifying new markers, diagnosing patients with a biological state of interest, and marketing / commercializing such diagnostics. The present invention relates to systems and methods of greater sensitivity, specificity, and / or cost effectiveness.

The invention provides a Parkinson's disease freezing gait prediction method and device based on eye movement characteristics, equipment and a medium. The method comprises the following steps: acquiring target information corresponding to a target object; the target information comprises target demographic data, target clinical features and target eyeball motion parameters collected at a certain time point, and / or target demographic data, target clinical features and target eyeball motion parameters continuously collected according to a preset frequency within a preset time; and inputting the target information into the target frozen gait prediction model, so that the frozen gait prediction model analyzes and processes the target information to obtain a static frozen gait risk assessment result and / or a dynamic frozen gait risk assessment result corresponding to the target object. According to the scheme, through multi-dimensional integration and collaborative verification of traditional clinical phenotypes and objective indexes of eye movement, non-invasive efficient evaluation of frozen gait risks is achieved, and objective basis and key technical support can be provided for early diagnosis and early treatment of disease subtypes.

This invention discloses a method for establishing a mouse model of outer retinal tubular structures, which is obtained by specifically knocking out the Lss gene in the mouse retina. This invention utilizes CRISPR / Cas9 gene editing technology to construct a mouse model of outer retinal tubular structures by specifically knocking out the Lss gene in the retina. The mouse model of outer retinal tubular structures constructed by this method is similar to the clinical phenotype of this type of retinal disease, providing a relatively ideal experimental animal model for studying the developmental regulation of the mammalian retina and the pathogenesis of retinal degeneration.

The invention provides a pre-pregnancy carrier screening genetic counseling system based on a large language model, which is applied to the technical field of gene sequencing analysis, and comprises a variation acquisition module used for screening to-be-sequenced gene data of a user to obtain pathogenic abnormality site information, converting pathogenic abnormality site information, disease clinical phenotypes, family history and individual past history into high-dimensional vectors; the variation retrieval module is used for dynamically retrieving the high-dimensional vector and recalling Top-K medical data in the semantic vector index database based on the semantic vector index database; the inference analysis module is used for evaluating the fertility genetic variation risk according to the Top-K related medical data and the high-dimensional vector to obtain an evaluation result; and the multi-round dialogue module enables the user to obtain an evaluation result through multi-round dialogues. The method has the advantages that the content such as fertility risks and intervention suggestions can be automatically generated, dialogue interaction with the patient can be carried out, and therefore the clinical genetic consultation burden is relieved.

The application relates to the technical field of culture medium prediction, and discloses a serum-free culture medium component prediction method based on artificial intelligence. The method comprises the following steps: constructing a phenotype condition vector according to a cell type identifier and a cell phenotype specification parameter, constructing a phenotype-formulation pairing data set according to historical culture batch data, training a conditional variational autoencoder with the phenotype condition vector as a conditional constraint, sampling a latent variable from a standard normal distribution, decoding the latent variable through a decoder to obtain a candidate culture medium formulation set, screening a candidate formulation with the minimum Euclidean distance through a forward verifier, and obtaining a recommended culture medium formulation. The application solves the problem that an existing serum-free culture medium component prediction method based on artificial intelligence cannot generate a culture medium formulation in reverse according to a clinical phenotype quality specification because the number of cells is taken as an optimization target.

This invention provides a method and system for integrated data correlation analysis based on clinical features and molecular indicators, belonging to the field of medical data processing and intelligent prediction technology. The method includes: preprocessing collected clinical feature data and molecular indicator data to obtain a standardized structured dataset; extracting key clinical feature subsets and key molecular indicator subsets from the standardized structured dataset, and fusing them to generate a unified multidimensional fusion feature vector; dynamically calibrating at least three clinical phenotypic benchmarks in the unified multidimensional fusion feature vector using a clinical feature data spectrum, whereby the clinical phenotypic benchmarks are defined based on age feature spectrum nodes, infertility course trajectory critical scaling, and endometrial thickness quantification cutoff values. This invention can effectively complete the integrated correlation analysis of clinical features and molecular indicators and predict pregnancy outcomes, meeting the needs of precision clinical diagnosis and treatment.

The invention discloses a hepatitis B virus combined mutation detection method and device based on high-throughput sequencing. A novel detection and analysis method is established on the basis of targeted amplicon sequencing, the combined mutation of a basic core promoter and a front core region on a single virus DNA molecule can be accurately analyzed, and the relevance of the combined mutation with clinical phenotypes and the influence of the combined mutation on virus functions are further explored on the basis of the combined mutation. The invention provides a new technical means for detecting the combined mutation in the specific region of the hepatitis B virus, makes up for the deficiency of the recognition capability of the traditional detection method on the combined mutation, not only perfects the research on the mutation in the specific region of the hepatitis B virus, but also is beneficial to the individualized diagnosis and treatment of the hepatitis B virus and the prognosis evaluation of diseases.

The invention discloses an ankylosing spondylitis spinal injury severity prediction model construction method and system, and the method comprises the following steps: S1, carrying out the data collection of an ankylosing spondylitis patient, the data including epidemiological data, clinical phenotypes and laboratory detection indexes; s2, layering the risk layers, and dividing a spinal injury severity high-risk group and a spinal injury severity low-risk group by taking mSASSS = 25 as a threshold value; s3, eliminating radiology dependent variables through logistic regression analysis, and screening out independent predictive factors; s4, constructing a spinal injury severity prediction column graph model based on the independent prediction factors; s5, vertically mapping the measured value of each index of the patient to a scale axis corresponding to the column diagram to obtain a single score; and after the total score of the six items is accumulated, positioning is performed on a risk coordinate axis to obtain a predicted risk probability. The method improves the disease prediction capability, is high in clinical universality, is convenient and efficient to operate, and has a treatment intervention guidance value.

The disclosure provides a pre-pregnancy carrier screening genetic counseling system based on a large language model, applied to the technical field of gene sequencing analysis, comprising a variation acquisition module for screening the user's to-be-sequenced gene data to obtain pathogenic abnormal site information, and converting the pathogenic abnormal site information, disease clinical phenotype, family history and personal past history into a high-dimensional vector; a variation retrieval module for dynamically retrieving the high-dimensional vector based on a semantic vector index database and recalling Top-K medical data in the semantic vector index database; an inference analysis module for evaluating the reproductive genetic variation risk according to the Top-K related medical data and the high-dimensional vector to obtain an evaluation result; and a multi-round dialogue module, through which the user can obtain the evaluation result through multi-round dialogue. The advantage of the disclosure is that the reproductive risk, intervention suggestions and other contents can be automatically generated, and the disclosure can also interact with the patient through dialogue, thereby reducing the clinical genetic counseling burden.

The invention provides a nutrition intervention method and system for a patient with sarcopenia, and relates to the technical field of health data management. The method comprises the following steps: acquiring multi-modal data of a patient, wherein the multi-modal data comprises basic information of the patient, body composition data, clinical phenotype data, nutrition evaluation data, biochemical index data, muscle function data and exercise preference data; determining the nutritional status, risk factors and intervention direction of the patient based on the multi-modal data; based on the intervention direction, generating an individualized nutrition scheme according to a quantity-mass-opportunity balance mechanism and a nutrition intervention mechanism; wherein the nutrition intervention mechanism comprises an intake mechanism, a nutrition quality mechanism and a feeding opportunity mechanism, the intake mechanism is used for determining total energy intake and a nutrition formula, the nutrition formula comprises the variety, the weight and the cooking mode of food materials, and the nutrition quality mechanism is used for determining nutrition preparations needing to be fortified and supplemented. The feeding opportunity mechanism is used for determining daily meal time distribution and cooperation timing with the exercise scheme.

The invention relates to a big data-based METTL14 action mechanism analysis method and system, and provides a multi-modal omics data-oriented causal structure modeling and intelligent prediction method for the problem of causal inference of a regulation relation of METTL14 and a target gene (such as PTEN) thereof in gastric cancer. The method comprises the following steps: firstly, acquiring a transcriptome, an m6A methylation modification group and clinical phenotype multi-modal original data from a public database, marking and pre-processing the data, unifying the data to a potential space through high-dimensional feature mapping, and constructing and optimizing cross-modal causal embedding representation and a graph structure; the causal link mining is realized by combining the variational self-encoding and the graph convolutional network, and the signal-to-noise ratios of different modes are dynamically weighted, so that the structural analysis precision is improved. Anomaly detection and adaptive correction are further introduced to ensure the credibility of the causal chain. Integrating external biological experiment data to carry out model fine tuning, and supporting dynamic introduction of new features. And finally, outputting an interpretable regulation and control prediction result. According to the scheme, the reliability and generalization ability of causal inference under multi-modal data are improved, and mechanism support is provided for targeted intervention.

This invention provides a computer-aided analysis system based on high-throughput sequencing data and knowledge graphs, including a data access agent, a quality control analysis agent, a species annotation agent, a knowledge graph reasoning agent, a report generation agent, and an agent scheduling center. The data access agent acquires sequencing data and clinical phenotypic data; the quality control analysis agent performs quality control using the Illumina NovaSeq platform, V3-V4 primers, the SILVA v144 database, Q30 ≥ 90%, and sequences ≥ 50 bp; the species annotation agent performs species classification annotation based on a reference database; the knowledge graph reasoning agent performs association analysis based on a microbiome-disease-intervention knowledge graph, using ET_MGNN and RTGN models, including 264 search keywords covering dental caries, periodontitis, oral cancer, Alzheimer's disease, diabetes, and heart disease; the report generation agent generates personalized analysis reports and intervention suggestions; and the agent scheduling center manages and dynamically schedules all agents, forming a complete closed loop of "perception → analysis → diagnosis → intervention → execution → feedback".

This application relates to the fields of bioinformatics and medical big data technology, specifically disclosing a digestive tumor risk assessment system based on multi-omics data. The system includes: a multi-source heterogeneous data acquisition terminal, a data standardization preprocessing module, a broad-spectrum digestive system risk assessment engine, an organ-specific feature decoupling module, a multimodal cross-omics fusion reasoning module, and a clinical decision support and early warning terminal. This solution simultaneously acquires ctDNA methylation, serum indicators, and clinical phenotype data, uses the assessment engine to evaluate overall risk, and extracts tissue-specific features through methylation map comparison. Combined with a dynamic weight allocation model, it outputs the targeted risk probabilities for the esophagus, stomach, and colorectal region. This application improves screening sensitivity through multi-dimensional data fusion, solves the problems of reliance on a single data source and inability to locate lesions, and achieves accurate organ-specific analysis and early warning.

The application provides a kind of based on deep learning's radiation pneumonia prediction method and system.The method comprises the following steps: S1, obtains DICOM image, radiotherapy data, clinical data and immunotherapy time series data, and carries out pre-processing;S2, constructs multi-channel three-dimensional structure tensor based on DICOM image;S3, obtains multi-channel input tensor X according to planned CT volume, dose volume and three-dimensional structure tensor;obtain time series characteristic vector and clinical phenotype characteristic vector according to radiotherapy data and clinical data;S4, construct fusion prediction model, and process three-dimensional structure tensor, time series characteristic vector and clinical phenotype characteristic vector, obtain the risk probability of radiation pneumonia;S5, based on the dynamic update of input and re-prediction of updated patient data.The application realizes the deep fusion and dynamic update of multi-modal information, can provide high-precision, risk prediction with spatially interpretable, and provides decision support for clinical intervention.

The invention discloses an AI high-flux cardiovascular medicine screening system, and relates to the field of cardiovascular medicine screening. Comprising the following steps: establishing a cardiovascular medicine multi-modal database, integrating compound structure data, biological activity data, genomics data and clinical phenotype data, and performing data cleaning, normalization and feature extraction by adopting a standardized process; the method comprises the following steps: establishing a multi-modal drug characterization learning model based on a deep learning framework, processing molecular two-dimensional structure features by using a graph neural network, processing molecular conformation information by using a three-dimensional convolutional network, processing physical and chemical parameters by using a full-connection network, and carrying out heterogeneous feature fusion and unified vector representation through a cross-modal attention mechanism. According to the method, an intelligent screening system covering the whole drug discovery process is constructed, so that collaborative precise prediction and multi-objective optimization of the activity, toxicity and pharmacokinetic properties of the compounds are realized, and the efficiency and success rate of discovery of the cardiovascular lead compounds are remarkably improved.

The invention provides a biological safety detection big data analysis method and system based on cloud computing, and relates to the field of safety detection.The method comprises the steps that multi-source heterogeneous data such as gene sequences, clinical phenotypes and space-time tags in a cloud platform are integrated, a time decay function and a geographic risk weight matrix are set, and the weighted risk support degree of each feature item is calculated; screening the feature item with the support degree higher than a first threshold value as a high-risk feature item; the tuple set is scanned in a descending order according to the high-risk feature items and the support degrees of the high-risk feature items, a context path coding FP tree is constructed, and besides feature identifiers and the support degrees, nodes of the FP tree further comprise path context vectors recursively updated along a father node path by using a gating loop unit; recursively mining the FP tree to generate candidate high-risk association modes, and calculating the cohesion and sequence validity of each candidate high-risk association mode; the candidate modes meeting the conditions that the weighted risk support degree is higher than a first threshold value, the cohesion degree is higher than a second threshold value and the sequence effectiveness is higher than a third threshold value are taken as biological safety high-risk association rules and output.

The application provides an IBS micro-ecological transplantation intelligent prediction method and system based on multi-omics driving, and relates to the field of biomedical technology. The method comprises the following steps: establishing a multi-omics data fusion subsystem to collect target patient metagenome, metabolome, host genome and clinical phenotype group data; inputting the data into a bacterial flora-metabolite joint network analysis model to construct an interaction network and extract features; generating a correlation matrix based on the features and host genome data and calculating an index; combining the clinical phenotype data and the index to generate an index through a dynamic response algorithm; and using a transfer learning framework to jointly model and output a therapeutic effect prediction result. The system comprises data acquisition, network analysis, correlation calculation, dynamic response and joint modeling modules. The application integrates multi-omics data, accurately mines the relationship between flora and host, realizes intelligent prediction of micro-ecological transplantation efficacy, provides strong support for IBS personalized treatment, and has data processing and security guarantee measures.

The invention discloses a congenital glaucoma animal model and a construction method and application thereof, and relates to the technical field of animal models. The construction method comprises the step of enabling the TRPA1 gene of a target animal to be not expressed or to be inhibited in expression through a gene editing technology so as to obtain the TRPA1 gene knockout or low-expression animal model. The model can effectively simulate the clinical phenotype of human congenital glaucoma, is helpful to understand the pathogenesis of glaucoma, and provides a simple, reliable and economic animal model closer to the phenotype of a patient for subsequent disease research.

The invention discloses a biological specimen-based special-type interstitial lung disease association analysis method, which comprises the following steps of: acquiring multi-dimensional omics and pathological characteristic data through a biological specimen omics data mining platform, and realizing characteristic differentiation grouping through a lung interstitial characteristic deep clustering identification algorithm, inputting the grouped data into an interstitial lung disease subtype typing prediction model, completing preliminary typing in combination with clinical phenotypes, capturing a pathological feature dynamic change rule by using a specimen pathological feature time sequence evolution model, integrating clustering, typing and time sequence evolution results through a platform feature fusion module to construct a feature set, and carrying out classification on the feature set; and establishing a parameter mapping relation of each dimension through a correlation analysis module. According to the method, feature input optimization, sequential sequence analysis and multi-dimensional feature efficient fusion are realized, the pertinence and reliability of correlation analysis are comprehensively improved, and technical support is provided for precise diagnosis and treatment of diseases.

The invention discloses a biomarker for auxiliary diagnosis of SLE (systemic lupus erythematosus) and application of the biomarker, and belongs to the technical field of biology. The invention discloses one of an anti-NPM1 antibody, an anti-RPLP2 antibody and an anti-MX1 antibody or a combination of any two or three of the anti-NPM1 antibody, the anti-RPLP2 antibody and the anti-MX1 antibody, which can be used as a biomarker for auxiliary diagnosis of SLE. And the kit can be used for auxiliary monitoring of SLE disease activity and auxiliary diagnosis of SLE (combined anti-phospholipid antibody) with clinical phenotypes of arterial thrombosis, microangiopathy, heart valve enuresis and thrombocytopenia, and has good diagnosis efficiency.