406 results about "Genomic data" patented technology

A system for recovering information lost during genomic data compression employs a quality-driven approach using neural networks. The system evaluates the importance of genomic regions through a quality analysis engine that assigns quality scores, while a rate control engine determines optimal compression rates based on these scores. A specialized neural network recovers lost information from correlated genomic datasets that have undergone lossy compression, utilizing recurrent layers for feature extraction and a channel-wise transformer with attention to capture complex relationships between data channels. The neural network architecture incorporates a deblocking network that combines these components to effectively reconstruct compressed data. A decoder receives and decompresses the data, then processes it through the neural network to recover information lost during compression. This adaptive system ensures critical genomic information is preserved while maximizing compression efficiency.

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.

A computer-based adaptive decision support system for radiotherapy, including: • a patient data acquisition module configured to acquire patient-specific clinical data, including an anatomical image, a physiological signal, and genomic data; • a preprocessing and feature extraction modality compatible with normalizing, preprocessing and extracting statistical features from the acquired data; • a status estimator used to provide a dynamic representation of the patient's treatment evolution status based on radiation, biological, dosimetric characteristics; • customized action rescaler to define a series of clinically meaningful treatment adjustments depending on the patient's current condition; • a reward function engine used to calculate therapeutic outcome scores based on the probability of tumor control, the probability of complications in normal tissue, and other predetermined factors; • a reinforcement learning agent that can learn and update treatment adaptation policies based on deep reinforcement learning techniques; • a clinical decision dashboard that provides recommended treatment adjustments and personalized interaction with the physician; and • a clinical integration interface adapted for exporting the customized treatment plan to an external treatment planning or delivery system.

The invention relates to the technical field of biological information, in particular to an immunoreaction evaluation method based on tumor neoantigen activity sorting. The method comprises the following steps: obtaining genome data of tumor and normal tissues through whole exon sequencing, extracting multi-dimensional features of candidate somatic mutation, and screening by combining a Gaussian mixture model and a Transform model to obtain a high-confidence mutation genome set; predicting the HLA genotype of a patient based on sequencing data, translating and mutating into a peptide fragment, predicting the binding affinity of the peptide fragment and an MHC molecule by using an XGBoost model, and calculating a new antigen activity score sequence in combination with various parameters; and finally, synthesizing a new antigen peptide fragment according to a sorting result, carrying out in-vitro co-culture to detect an IFN-gamma secretion result, and dynamically optimizing a characteristic combination coefficient through a PPO algorithm to realize intelligent iterative updating, so that the accuracy of new antigen screening and the immunoreaction prediction capability are remarkably improved.

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 multi-modal cancer survival prediction method based on a potential differentiation variational auto-encoder, and the method comprises the following steps: carrying out the tissue region segmentation of an input full-view digital slice, extracting the pathological features, and carrying out the grouping extraction of the grouping features of input genome data according to the function category; generating compressed pathological feature potential distribution through an information bottleneck theory and an attention mechanism; potential distribution of genome data is learned through global posteriori, specific potential variables are generated through a functional differentiation network, and missing genome features are reconstructed; integrating pathology and genome posteriori based on an expert product technology, and introducing alignment loss to constrain consistency of posteriori distribution; and screening survival related features through a co-attention mechanism, and outputting a survival probability and risk layering result. By adopting the multi-modal cancer survival prediction method based on the potential differentiation variational auto-encoder, the problem of calculation redundancy is solved, multi-modal joint distribution estimation under missing data is realized, and the clinical applicability is improved.

The invention discloses a biological complex trait key gene identification method, device and equipment and a medium, and the method comprises the following steps: obtaining biological reference genome data, and carrying out orthologous gene class group division on a protein sequence coded by the biological reference genome data; determining a characteristic value data set according to the number of the subgroup sequence and the gene number of the subgroup sequence obtained after division; obtaining each piece of biological phenotype data; utilizing a machine algorithm model to construct a phenotype matrix according to the biological phenotype data and the characteristic value data set so as to train a phenotype prediction model, and determining the influence weight of each subgroup sequence on the biological phenotype according to the phenotype prediction model; and according to each influence weight, determining a key gene for regulating and controlling the biological complexity. The method provided by the invention can realize more accurate, efficient and comprehensive identification of key genes of prokaryotes and high biological complexity characters.

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.

Systems and methods for analyzing multimodal data using one or more large language machine learning models may include inputting medical image data to a trained image-based machine learning model and generating medical image data embeddings; inputting genomic data to a trained genomics machine learning model and generating genomic data embeddings; inputting text data to a trained large language machine learning model, and generating a mapping of extruded data; and feeding the medical image data embeddings, the genomic data embeddings, and the mapping of extruded data to trained large language machine learning model at a foundation layer, the trained large language machine learning model at the foundation layer being trained to generate one or more of a data and analytics report, a diagnostic analysis report, a large language model agent, or user data for display in a user interface at an output device.

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.

The invention discloses an identification and analysis method for unknown pathogenic microorganisms, which comprises the following steps: filtering out genome sequences with low integrity, pollution and tag errors, and establishing a high-quality virus identification database; constructing a virus host prediction model through a machine learning algorithm; unknown pathogenic microorganisms are identified and analyzed, potential hosts or pathogenicity of the unknown microorganisms are identified, and whether the unknown microorganisms are unknown pathogenic viruses or bacteria or not is further judged. On the basis of metagenome data analysis, potential unknown pathogenic microorganisms in samples of human bodies, environments and the like can be identified more accurately.

The invention discloses application of a gene marker in early screening of esophagus, stomach and intestine multiple cancer species, an early screening model construction method and a detection device, and belongs to the technical field of early noninvasive detection of digestive tract tumors. By analyzing the whole genome characteristics of circulating free DNA in peripheral blood, a novel multi-cancer-species screening system is established. On the basis of low-depth whole genome sequencing data, molecular markers in three dimensions, namely a genome copy number variation mode, a DNA fragment distribution characteristic with a specific length and an epigenetics signal of a transcription initiation region, are emphatically detected. An advanced converter neural network architecture is adopted, and the model can efficiently capture complex feature association in a whole genome range through a specific self-attention mechanism. The model design particularly considers the particularity of genome data, introduces an adaptive position coding system, and accurately reflects the spatial distribution relationship of DNA fragments on chromosomes. Therefore, the system can still maintain excellent detection performance under extremely low sequencing depth.

The invention provides an employee health risk monitoring method and system based on big data, and relates to the technical field of health management and data analysis, and the method comprises the steps: S1, collecting the multi-modal health data of an employee, including but not limited to physiological data, behavior data, working environment data, social interaction data and genome data, and S2, fusing and analyzing the collected multi-modal health data by adopting an integrated neural network model and an adaptive learning algorithm. According to the big data-based staff health risk monitoring method and system, through the combination of the integrated neural network and the deep causal inference model, the multi-modal health data of the staff can be deeply analyzed, and the complex causal relationship between the health data can be effectively identified. Through the dynamic analysis of the historical health data, the current health state and the working environment of the employees, the model can achieve the precise health risk prediction, and provides personalized health intervention suggestions according to the individual differences of the employees.

A system is disclosed for discreetly assessing the compatibility of two or more human genomes across diverse elements, activities, and engagement platforms relevant to potential mating scenarios. The genomic data is subjected to encryption, with the option of employing homomorphic encryption to safeguard user privacy and security. Processing of the data is facilitated through a personal health database processing system, which may be cloud-based or edge-based. The application of homomorphic encryption ensures that the genomic information of individual users remains encrypted during processing, with the outcome limited to the display of progeny compatibility to the respective end users.

The invention discloses a lung squamous cell carcinoma drug discovery method and system based on disturbance response scanning analysis, and belongs to the field of biological medicines.The method comprises the steps that lung squamous cell carcinoma protein expression profile data is downloaded and preprocessed, a robust network module is constructed and multi-scale analysis and evaluation are conducted, and drug genomic data are obtained based on drug genomic data. Predicting the drug sensitivity of the patient by using machine learning, and generating a DRN through difference analysis of the drug sensitivity; predicting drug-target protein binding affinity in combination with deep learning, quantifying node sensitivity through a PRS technology, calculating a DPI disturbance score and ranking drug priorities; high-ranking drugs are screened through comprehensive sensitivity analysis and literature investigation; a drug for a lung squamous carcinoma cell line is screened out through a pre-experiment and is relocated. According to the framework developed by the invention, by integrating proteomics, pharmacogenomics and dynamic network analysis, systematic analysis is provided for relocation drug identification of lung squamous cell carcinoma, and good news is brought to treatment of lung squamous cell carcinoma.

The invention provides a design method of a specific gene probe for detecting pathogenic microorganisms, and belongs to the technical field of microorganism detection. Comprising the following steps: acquiring reference genome data of target pathogenic bacteria, reference genome data of all species belonging to the same genus as the target pathogenic bacteria and genome data of all strains under the target pathogenic bacteria species; comparing the reference genome data of the target pathogenic bacteria with the reference genome data of all target pathogenic bacteria congeneric species by using MUMmer to obtain all fragments in the reference genome of the target pathogenic bacteria, wherein the base number of the fragments is greater than or equal to 20 when the fragments are continuously compared with the reference genomes of other congeneric microorganisms, and the base number of the fragments is greater than or equal to 20 when the fragments are continuously compared with the reference genomes of other congeneric microorganisms; recording starting base sites and ending base sites of all fragments; breaking a reference genome of the target pathogenic bacteria into a 50nt k-mer set, and filtering out non-specific fragments in the 50nt k-mer set to obtain an initial candidate probe set; filtering the initial candidate probe set to obtain a candidate probe set 3; and filtering the candidate probe set 3 to obtain a specific probe set. According to the design method, the specific probe is directly mined in the genomic data of the pathogenic microorganisms, large-scale probe design work can be competent, the designed probe has excellent resolution, the accuracy of pathogenic microorganism detection can be improved, and accurate detection of the pathogenic microorganisms is achieved.

Techniques for performing genomic security-related control of a digital ecosystem are disclosed. In embodiments, the digital ecosystem includes an ecosystem VDAX that maintains a progenitor genomic data set corresponding to the digital ecosystem, generates a plurality of respective progeny genomic data sets based on the progenitor genomic data set, and allocates the progeny genomic data set to a respective progeny VDAX of a plurality of progeny VDAXs, wherein the progeny VDAX establishes unique non-recurring engagements with other progeny VDAXs in the digital ecosystem based on the respective progeny genomic data set allocated to the progeny VDAX without any further interaction from the ecosystem VDAX. The ecosystem VDAX also controls a genomic topology of the ecosystem by selectively updating one or more of the progeny genomic data sets to affect an ability of specific progeny VDAXs to engage with other VDAXs in the ecosystem.

The invention discloses a system for prognostic risk prediction of colorectal cancer, and relates to the technical field of medical artificial intelligence, and the system is technically characterized by comprising a data acquisition and preprocessing module, a multi-modal feature extraction module, a multi-modal feature fusion module, a prognostic risk prediction module and a visualization and interpretation module. The system extracts key features of a patient by collecting pathological images, genome data and clinical information of the patient, deep fusion of multi-modal data is realized by using an attention mechanism, and a comprehensive feature vector is generated. Based on a deep learning model, the system efficiently predicts the prognosis risk of a patient, and the contribution of key features to a prediction result is displayed by adopting explanatory technologies such as SHAP and the like. The system has the characteristics of multi-modal data integration, efficient prediction and transparent result, and can provide intuitive risk stratification results and personalized treatment suggestions for doctors. Through multi-center data verification, the system is excellent in accuracy, generalization ability and clinical applicability.

The present invention discloses a method and system for managing the side effects of medication for tumor patients based on big data analysis, the method comprising the following steps: collecting and preprocessing patient data and drug data, extracting patient characteristics and drug characteristics, wherein the patient data includes genomic data provided by the patient; constructing a prediction model, including a time series analysis sub-model, a gene-drug interaction analysis sub-model and a risk score generation sub-model, for using drug characteristics and patient characteristics as inputs of the prediction model to predict the risk score of different types of side effects in the patient; inputting the basic information, medication information and genomic data of the current patient into the trained prediction model to obtain a risk assessment of various side effects in the current patient; according to the risk assessment results, selecting a personalized adjustment suggestion suitable for the patient from a preset intervention program library and providing it to the current patient. The present invention can effectively improve the scientificity and accuracy of the management of medication side effects during the treatment stage.

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.

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.

The invention belongs to the field of molecular genetics, and particularly relates to an SNP (Single Nucleotide Polymorphism) molecular marker combination for genetic relationship identification of wuzhu cattle and application of the SNP molecular marker combination. The SNP marker combination is composed of 140 high-polymorphism SNP sites distributed on a plurality of chromosomes of a bovine whole genome, the average distance between the sites is reasonable, and the SNP marker combination has good genome coverage. The selected SNP site has high allele frequency (MAF), expected heterozygosity (He) and polymorphic information content (PIC), and the cumulative exclusion probability is 0.9999. King and Plink software are combined to deduce a genetic relationship coefficient (Kinship) and an IBD shared value (PIHAT) between samples, and a result shows that the marker combination can realize a genetic relationship recognition effect equivalent to that of whole genome SNP data in Wuzhu white cattle (grassland white cattle), and the scientificity and practicability of the marker combination are verified. The SNP molecular marker combination can be widely applied to the processes of germplasm resource management, pedigree correction and cattle breeding, and has good popularization prospects and economic benefits.

Systems and methods for generating a prediction of a response of a patient to a medication to treat a disease are provided. The system may obtain genomic data, medical image data, and clinical data of the patient. The system may provide the genomic data to a genomic transformer trained to extract exomic data of the patient associated with the disease. The system may provide the exomic data, the medical image data, and the clinical data to a disease prediction model trained to generate the prediction of the response of the patient to the medication. The system may provide the prediction to a user device.

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 discloses a beef cattle multi-variety genome data analysis and management method and system, and the method comprises the following steps: data collection and integration: obtaining beef cattle original genome sequence data, building a genome data storage library, and adding a unique identification label for the data of each variety; performing data preprocessing and standardization: detecting variation points, performing standardization processing on the detected variation points, and meanwhile, performing encoding processing on genotype data of the variation points; multi-variety genome feature analysis: identifying beef cattle varieties with rich genetic diversity and relatively deficient beef cattle varieties, obtaining potential variety subgroups or variety combinations with similar genetic backgrounds, and mining character associated sites ubiquitous in different varieties or specific to specific varieties; and performing data visualization and report generation: performing visual display on a result obtained by analysis. According to the method, comprehensive, systematic and efficient analysis and management of beef cattle multi-variety genome data can be realized, and powerful support is provided for beef cattle genetic breeding.

The invention provides a method for evaluating a set of input data, the input data comprising at least one of: clinical data of a subject; genomic data of a subject; clinical data of a plurality of subjects; and genomic data of a plurality of subjects, using a deep learning algorithm. The method includes obtaining a set of input data, wherein the set of input data comprises raw data arranged into a plurality of data clusters and tuning the deep learning algorithm based on the plurality of data clusters. The deep learning algorithm comprises: an input layer; an output layer; and a plurality of hidden layers. The method further includes performing statistical clustering on the raw data using the deep learning algorithm, thereby generating statistical clusters and obtaining a marker from each statistical cluster. Finally, the set of input data is evaluated based on the markers to derive data of medical relevance in respect of the subject or subjects.

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.