177 results about "Genomic data" patented technology

A system and method for processing genomic data using dynamic latent manifolds that transforms multi-modal genomic datasets into geometric representations within a curved manifold space. The system receives genomic datasets including DNA sequences, genetic variants, and expression data, then extracts biological features and assesses importance using trained neural networks. Manifold curvature values are computed based on biological significance, and genomic data is embedded as geometric structures where semantic relationships are represented through distance and curvature properties. The system generates compression pressure fields that influence processing decisions and computes optimal geodesic paths through the manifold to minimize cognitive action functionals. Adaptive compression rates are determined for different genomic regions based on geometric properties and biological importance. The manifold structure evolves through use, strengthening frequently accessed pathways while applying thermodynamic decay to unused concepts. The system supports hierarchical organization across biological scales, reversible navigation, and federated learning capabilities that enable privacy-preserving collaboration.

The invention provides a laryngeal cancer multi-mode prognosis prediction method and a laryngeal cancer multi-mode prognosis prediction system fusing a CT (Computed Tomography) image and a ViT model. Relates to the technical field of biomedical images. The method comprises the following steps: acquiring and preprocessing multi-modal data of a laryngocarcinoma patient; carrying out lightweight compression, redundant information screening and robustness training on the ViT model to obtain an optimized ViT model; extracting depth features of the CT image data based on the optimized ViT model, and performing multi-stage fusion on the depth features and clinical and genome data to construct a prognosis prediction model; and performing risk stratification on the patient according to a prognosis prediction result predicted by the prognosis prediction model, and outputting treatment guidance suggestions based on the risk stratification. Through ViT model optimization, multi-modal data fusion and clinical adaptation design, precise prediction and personalized treatment guidance of laryngocarcinoma prognosis are realized, and the problems of insufficient image degradation processing, low model deployment efficiency and the like in existing laryngocarcinoma prognosis prediction are solved.

The invention relates to the technical field of genome data analysis, and discloses an intelligent system, method and equipment for assisting multi-step genome data analysis, and the system comprises a dialogue agent which is used for generating a corresponding answer according to a question of a user, or reading an analysis plan file generated by a workflow agent, generating an analysis interpretation text for the analysis plan file; the workflow agent is used for generating a structured task execution plan according to the to-be-executed analysis task and executing the to-be-executed analysis task; and the modeling analysis agent is used for generating a configuration file and a script based on the user request, constructing a model and generating an analysis result corresponding to the user request in combination with the workflow agent. Through multi-agent cooperation, task division and cooperative scheduling are realized, each agent independently completes task planning, execution control, model analysis and other functions, the bottleneck problem of processing of a traditional single model in a complex process is avoided, error accumulation is reduced, and the execution efficiency and stability of the whole process are improved.

The invention discloses a small CRISPR (clustered regularly interspaced short palindromic repeats)-Cas gene editing system and application thereof. According to the invention, based on microbial genome and metagenome data, a class of CRISPR-Cas family protein is mined through a biological information method, and is named as Cas12r. A CRISPR-Cas12r editing tool constructed on the basis of the gene can realize gene editing in prokaryotic or eukaryotic cells. The CRISPR-Cas12r gene editing system obtained by the invention has the characteristics of miniaturization and various PAM types.

The invention discloses a medical image automatic identification system based on a neural network, and relates to the technical field of medical image identification. The method is used for solving the problem that early recognition of neurodegenerative diseases is difficult due to medical image and genome data splitting and poor model interpretability in the prior art. The method comprises the following steps: firstly, extracting multi-scale features of a brain structure through a three-dimensional convolutional neural network and a self-attention mechanism, calculating a multi-gene risk score based on a risk site, and encoding the score into a feature vector; secondly, using a cross attention mechanism to take gene features as query vectors, fusing the gene features with image features, and generating brain structure anomaly features under gene regulation; then, gradient weighting class activation mapping is applied to generate a visual thermodynamic diagram, and gene-image association weight weighting is combined to construct a brain region risk distribution diagram; and finally, a high-risk brain region space coordinate set is extracted through threshold segmentation, and an accurate quantification basis is provided for early recognition.

A system and methods for upsampling of decompressed genomic data after lossy compression using a neural network integrates AI-based techniques to enhance compression quality. It incorporates a novel deep-learning neural network that upsamples decompressed data to restore information lost during lossy compression, taking advantage of cross-correlations between genomic data sets.

A genomic data analyzer workflow may be configured to identify, with a variant annotation module, subsets of patient variants which match at least one medical reference variant database entry, even if the variant calling information in genomic data analyzer workflow and the database use different variant representations of SNP, MNP, INDELS and DELINS. In particular, database variants which are included into a subset of patient variants may be identified even if they do not exactly match the corresponding strings. The variant annotation module may be adapted to apply a branch-and-bound-like algorithm to efficiently process all possible subsets of patient variants in a genomic region.

The disclosed multiscale framework includes innovatively coupling genome-scale metabolic models with an agent-based model and an adapted continuum model of the biofilm; employing a systematic bottom-up approach to identify interrelationships between local substrate and mediator transport and the dynamic biofilm characteristics; and elucidating the interdependence of genomic data and microscale biofilm properties, thereby enabling a deeper understanding of the behavior of species within the biofilm.

The invention provides a microbial ecological interaction mechanism determination method and device, and relates to the technical field of species correlation research. The method comprises the following steps: determining N microorganisms and abundance thereof based on metagenome data of a target environment sample; determining the abundance grade and correlation based on the abundance of the ith microorganism and the jth microorganism; carrying out metabolic function annotation on the i microorganism and the j microorganism based on a reference genome, and calculating a function overlapping ratio; determining a community construction mechanism based on the abundance correlation and the function overlapping ratio; based on the function overlapping difference, determining a function overlapping grade change; and determining the ecological interaction mechanism of the i and j microorganisms according to the information. The method can be used for systematically revealing a species coexistence mechanism and a metabolic interaction relationship thereof in the microbial community.

The invention provides a non-lineage animal pairing evaluation method based on genome data, and belongs to the technical field of bioinformatics and protection genetics, the method comprises the following steps: firstly, obtaining whole genome sequencing data of all individuals in a population, and carrying out quality control to obtain a high-quality SNP site set; aiming at all possible male and female pairing combinations in the population, calculating an inter-parent genetic coefficient depKin, an inter-parent heterozygous difference ratio HDR and an inter-parent potential risk load index GRLI, and predicting a fixed proportion Proh of ROH of offspring; performing z-score standardization processing on the four genetic indexes: performing principal component analysis on standardized data, extracting the first two principal components PC1 and PC2, determining a weight according to a variance contribution rate, and calculating a pairing comprehensive score; and generating a pairing candidate recommendation list for each individual according to the comprehensive score, and screening a high-quality pairing scheme. The method disclosed by the invention can be completely independent of pedigree records, and genetic evaluation is directly carried out based on genome data.

The invention relates to the technical field of carbon emission processing, in particular to a visual building construction process carbon emission calculation method, which comprises the following steps of: endowing each building component with a component identifier, binding carbon footprint data of a building material corresponding to the building component in a construction stage, and correspondingly generating a plurality of target carbon units; combining the plurality of target carbon units to generate a carbon genome data model; according to a preset carbon emission factor, converting resource consumption data and construction activity data generated in the current building construction process into a carbon emission data stream; fusing the carbon emission data flow with the carbon genome data model, updating the carbon emission state of each target carbon unit in the building construction process, generating carbon emission distribution information mapped with the three-dimensional space position of the building and the construction progress of the building, and performing visual rendering on the carbon emission distribution information, the technical problem that significant deviation exists between the calculation result and the carbon emission actually generated in the construction process is solved.

This invention discloses a multi-scale prediction method and system for the maximum growth rate of soil microorganisms based on genomic features, involving the interdisciplinary fields of microbial ecology and genomics. The method includes: genomic data preparation, core genomic feature extraction, single-scale growth rate prediction, community-scale extrapolation, and result verification and output. The system includes a genomic data processing unit, a core feature extraction module, a growth rate prediction model, a community-scale extrapolation module, and a result visualization and verification unit. This invention overcomes the limitations of traditional culture methods and in-situ measurement methods, achieving high-throughput and accurate prediction of the growth rates of culturable and uncultured microorganisms, and solving the problems of low coverage, fragmented scales, and high costs of existing technologies. It is applicable to soil microbial function assessment, carbon cycle model parameterization, and ecosystem management.

The application belongs to the field of fish development, and particularly relates to a molecular marker for identifying Nibea albiflora sperm-induced Pseudosciaena heteroclita gynogenesis offspring and hybrid offspring, a primer set thereof, a kit and application. The primer set for identifying the molecular marker of the Nibea albiflora sperm-induced Pseudosciaena heteroclita gynogenesis offspring and hybrid offspring comprises primers F and R, the nucleotide sequence of the primer F is shown as SEQ ID NO. 1, and the nucleotide sequence of the primer R is shown as SEQ ID NO. 2. Based on genomic data analysis, the application develops the molecular marker F / R which can effectively identify the Pseudosciaena heteroclita gynogenesis offspring and hybrid offspring in the juvenile stage. The method comprises extracting genomic DNA of parents and offspring, PCR amplification and agarose gel electrophoresis detection, and finally distinguishing the gynogenesis individuals and hybrid individuals by whether the Nibea albiflora parent marker is contained in the electrophoresis band.

A system, method and apparatus include one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations for hardware-accelerated execution of a genomic data processing pipeline based on one or more user-selectable options presented via a graphical user interface. The operations include obtaining first data representing a selection of one or more of a plurality of user-selectable options submitted via the graphical user interface. One or more of the plurality of user-selectable options identify a particular genomic data processing pipeline. The operations further include configuring, using the application programming interface executed by the one or more computers, an integrated circuit to perform one or more hardware accelerated steps of a primary, secondary, and / or tertiary processing protocol of the particular genomic data processing pipeline.

A computer-implemented method (100) for representing a graph genome data structure in a memory of a computer system, the method comprising: receiving (120) a definition of a graph reference genome, comprising: (i) a plurality of graph genome edges, each specifying a sequence of characters, and (ii) a plurality of links representing connections between the plurality of graph genome edges; generating (130) a graph data structure for the received graph reference genome definition, comprising a reference sequence count specifying a number of a plurality of reference sequences; and storing (140) the generated graph data structure in memory.

The invention belongs to the technical field of pollution control, and relates to a soil pollution treatment method and system utilizing microbial remediation, and the method comprises the following steps: obtaining target remediation functional microbial agent genome and to-be-remedied site native microbiome metagenome data, and constructing a biological information basic data set; processing the data, and constructing an interaction network model containing microbial inoculum and native species nodes based on metabolic complementarity and ecological niche overlapping degree simulation calculation; analyzing the network topology structure to screen a key native co-generation node set, and generating a growth promotion demand map; determining a targeted metabolism regulation factor with targeted selectivity based on atlas reverse matching, and generating a targeted signal instruction; executing the instruction and putting a microbial agent, activating a synergistic node in situ and coupling with the microbial agent to construct a degradation function network; the method solves the problem that the colonization efficiency of the exogenous functional microbial inoculum is not high due to lack of accurate regulation and control on the native microbial community.

A method for constructing a database for discrimination of microorganisms according to the present disclosure includes: a step (S12) for acquiring genome data of two kinds of microorganisms; a step (S14) for predicting a group of proteins produced by each of the two kinds of microorganisms; a step (S16) for producing a list of mass-charge ratios of each of the two kinds of microorganisms; a step (S20) for calculating the degree of similarity between the lists of the mass charge ratios; a step (S32) for generating information that includes the fact that the two kinds of microorganisms cannot be discriminated by MALDI-MS when the degree of similarity is equal to or larger than a predetermined value; and a step (S36) for outputting the information.

Provided herein are methods and systems for detecting polynucleotide sequences encoding viral capsids in metagenomic data. The methods and systems disclosed herein may include a sequence alignment-based module, a gene-based data processing module, and further characterization of putative viral sequences to identify novel polynucleotide sequences encoding viral capsids in metagenomic data.

The application discloses a gene editing system CRISPR-Cas12p and application thereof. Based on microbial genomes and metagenomic data, the CRISPR-Cas12p protein of the CRISPR-Cas protein family is obtained by preliminary screening by using a Prodigal gene prediction tool, a Pfam database and HMMER software, and a gene editing system CRISPR-Cas12p is constructed. PAM preference and interference function identification show that the editing system has a PAM preference of 5'-TTC-3', can effectively realize targeted cutting by using long transcripts and double RNA hybrid chain transcripts respectively, and can realize editing of a target gene in prokaryotic and eukaryotic cells. The CRISPR-Cas12p gene editing system obtained by the application has a small protein component, is beneficial to delivery, can realize gene editing in prokaryotic and eukaryotic cells, and has a wide application prospect.

Genomic data is written to disk in a compact format by dividing the data into segments and encoding each segment with the smallest number of bits per character necessary for whatever alphabet of characters appears in that segment. A computer system dynamically chooses the segment boundaries for maximum space savings. A first one of the segments may use a different number of bits per character than a second one of the segments. In one embodiment, dividing the data into segments comprises scanning the data and keeping track of a number of unique characters, noting positions in the sequence where the number increases to a power of two, calculating a compression that would be obtained by dividing the genomic data into one of the plurality of segments at ones of the noted positions, and dividing the genomic data into the plurality of segments at the positions that yield the best compression.

The application provides an analysis method for complex evolutionary history based on deep learning, comprising: according to a preset species evolutionary history simulation sequence data, respectively as a model training set and a test set; determining the topological structure of the training set data, and labeling the training data in different topological structure proportions; constructing a convolutional neural network, training and testing the convolutional neural network with the data set, so that the error between the data prediction value and the label value is minimized; based on the trained convolutional neural network, analyzing real genomic sequence data, and combining other population genetics analysis methods, determining the evolutionary relationship and introgression site between different biological groups. The application uses comparative genome or population genome data, infers the topological structure between sequences through a deep learning algorithm, further evaluates the evolutionary relationship at the genome level, and identifies local introgression signals through the difference of the topological structure between different regions.

The application is a deep learning-based method for breeding and multiplying Penaeus vannamei, which comprises: obtaining genomic data of the initial population; obtaining specific trait data of the screened initial population; locating a specific gene sequence set according to the specific trait data and the genomic data; the gene sequence set is a gene number set related to the specific trait; and determining the breeding shrimp for breeding according to the gene number set. The application first breeds Penaeus vannamei populations of different families, obtains the trait phenotype data and genomic data of Penaeus vannamei under different families, determines the gene sequence meeting the trait phenotype requirements according to the correlation degree between the trait phenotype data and the genomic data, that is, locates the gene sequence related to the specific trait, and then selects the next generation of Penaeus vannamei according to the gene sequence set, or directly breeds the breeding shrimp with stable traits.

The invention relates to the technical field of metagenomics, discloses an automatic analysis method and system for pit mud metagenomic data, and aims at solving the problem that an existing method is poor in efficiency and accuracy, and the scheme mainly comprises the steps that a sequencing data type, a file path and analysis parameters are received; performing quality control on the original offline data; sequence assembly is carried out, and a contigs file is generated; carrying out assembly quality evaluation on the contigs file; carrying out genome binning by using at least two binning tools; integrating output results of the binning tool, and performing optimization based on a preset integrity threshold value and a preset pollution degree threshold value to obtain an optimized binning genome data set; evaluating and optimizing the integrity, the pollution degree and the strain heterogeneity of the binning genome; calculating coverage and relative abundance; performing species classification annotation and function annotation; and integrating the result data of the previous steps to generate an analysis report. According to the method, automatic analysis of metagenome data is realized, and the analysis efficiency and accuracy are improved.

The invention discloses a multi-mode lung cancer lifetime prediction method based on knowledge decomposition. The method comprises the specific steps that firstly, pathological section images and genome data are preprocessed, and features are extracted through a residual network and a self-normalization neural network; then decomposing the two modes into common, redundant and independent features by using a knowledge decomposition module comprising a cross attention encoder and an MLP encoder; and finally, using a Transfomer network to integrate the four features for training, and substituting a training result into a Cox risk scale model to obtain a risk coefficient. Wherein a knowledge decomposition module is designed, the knowledge decomposition module comprises a cross attention encoder used for separating common features S and redundant features C and an MLP encoder used for separating features P and G which are independent from each other in two modes, and essential differences and internal relations of four knowledge components of the SCPG are mathematically defined through four loss functions; and a set of fine collaborative constraint mechanism is formed.

The invention discloses a carbapenem drug resistance marker screening method and system based on a cross-species compressed Debrueine diagram and a medium. The method comprises the following steps: starting from whole genome sequencing data of gram-negative bacteria belonging to different species and carbapenem drug phenotypes of the gram-negative bacteria, constructing a compressed Debrueine graph based on cross-species joint data, and taking existence / deletion of nodes in the graph as unified genetic variation characteristics. Performing correlation analysis on the nodes and the drug resistance phenotypes by using a linear hybrid model to obtain a candidate node set related to the phenotypes; k-mer is extracted based on the candidate node sequence, and secondary statistical screening is completed in combination with chi-square test and mutual information; and finally determining a group of carbapenem drug-resistant genetic markers which can be applicable across species through a hierarchical feature selection strategy of random forest and XGBoost. Efficient dimension reduction of large-scale cross-species genome data, cross-species consistent variation representation and high-interpretability marker screening are achieved.

The invention relates to the technical field of data analysis, in particular to a patient medical information intelligent analysis method based on a big data artificial intelligence model, which comprises the following steps: processing large-scale and high-dimensional data according to clinical symptoms and disease degrees in multiple sources such as electronic health records, genome data and the like, extracting key features by utilizing a dimension reduction technology, and analyzing the key features; and information loss is reduced. The non-linear relation in the data can be identified and modeled, the complex mode is disclosed, and the medical data information of the patient can be analyzed more deeply. The refined analysis helps doctors to identify specific requirements of different patient groups, the effectiveness of clinical decisions is improved, and personalized medical treatment and resource optimization are realized.

Policy-based genomic digital data sharing facilitates a variety of sharing scenarios, including public access, tenant-to-tenant sharing, workgroup sharing, and access by external service providers. Genomic digital data can be published to the platform and controlled by access tokens that are generated based on access policies. The policies can support conditions that are evaluated at execution time and effectively place control of access to information in hands of the owning tenant. Sharing conditions can be easily specified to support various use cases, relieving administrators from excessive access control configuration.

The invention provides a causal inference method and device based on genetic variation, electronic equipment and a medium. The causal inference method comprises the following steps: acquiring whole genome SNP data and proteome data of a detection sample of a target population from a detection platform; performing whole genome association analysis on the whole genome SNP data to obtain outcome SNP data associated with the target phenotype; performing protein quantitative trait site analysis based on the whole genome SNP data and the proteome data to obtain exposure SNP data associated with the target exposure factor; and performing data preprocessing on the outcome SNP data and the exposed SNP data, and performing Mendel randomization analysis on the preprocessed outcome SNP data and exposed SNP data to obtain a causal relationship between the target exposure factor and the target phenotype. According to the method, the accuracy and reliability of Mendel stochastic analysis results are improved.

A genomic data analyzer may be configured to detect and characterize, with a variant calling module, genomic variants from next generation sequencing reads out of a pool of enriched genomic patient samples without suffering from next generation sequencing workflow biases such as those introduced by sequencing errors in particular in repeat patterns regions of the human genome such as homopolymers or heteropolymers. The variant calling module may estimate the probability distribution of the length of the repeat pattern for each patient sample and cross-analyze it against other samples in a single experimental pool to identify best-fit variant models for each pair of samples. The variant calling module may further group samples according to their matching best-fit variant models and identify which group of patient samples carries the wild type reference without the need for control data in the pool. The variant calling module may subsequently characterize the homozygous or heterozygous repeat patterns variants for each patient sample with improved specificity and accuracy even in the presence of next generation sequencing biases.

The invention discloses a dairy cow and live pig high-quality breeding method based on AI genomics, and relates to the field of breeding. Comprising the following steps: multi-dimensional data acquisition: aiming at a target breeding group, acquiring whole genome variation data, various phenotype data and environment management data of each individual, establishing unique identification association for all the data, and storing the data in a central database; data preprocessing and feature enhancement: performing quality control, filling and standardization processing on the genome data, and constructing an effective feature set for model training from the original data based on statistics and machine learning methods; and training an AI prediction model. By introducing the artificial intelligence deep learning model, the complex non-additive effect and gene-environment interaction between the genotype and the phenotype can be efficiently captured, the prediction precision of important economic characters is greatly improved, and earlier and more accurate selection and optimized hybridization are realized, so that the genetic progress is greatly accelerated, the breeding cost is reduced, and the method is suitable for large-scale popularization and application. The breeding efficiency and benefits are comprehensively improved.