517 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.

The invention discloses a tumor patient medication side effect management method and system based on big data analysis, and the method comprises the following steps: collecting and preprocessing patient data and drug data, and extracting patient features and drug features, the patient data comprising genome data provided by a patient; constructing a prediction model which comprises a time sequence analysis sub-model, a gene-drug interaction analysis sub-model and a risk score generation sub-model and is used for predicting risk scores of different types of side effects of the patient by taking the drug characteristics and the patient characteristics as input of the prediction model; inputting the basic information, the medication information and the genome data of the current patient into the trained prediction model to obtain risk assessment of various side effects of the current patient; according to the risk assessment result, personalized adjustment suggestions suitable for the patient are selected from a preset intervention scheme library and provided for the current patient. The scientificity and accuracy of administration side effect management in the treatment stage can be effectively improved.

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 discloses a neurology rehabilitation degree evaluation method and system, and relates to the technical field of medical rehabilitation evaluation.The method comprises the following specific steps that data collection is conducted, specifically, whole genome sequencing is conducted on a neurology patient, and whole genome data of the patient is obtained, the method comprises the following steps: collecting initial disease severity degree data of a patient, and evaluating the symptom expression of the patient when the patient sees a doctor for the first time by a professional neurology doctor according to a set disease evaluation standard. A key gene screening strategy and a synaptic plasticity index establishment method are applied to construct a multi-dimensional and more accurate neurology rehabilitation degree evaluation system, so that the neural function repair level is deeply known from the root level of nerve cell connection reconstruction; and accurate prediction and real-time evaluation of the rehabilitation process are realized through the personalized rehabilitation trajectory prediction model, so that the real rehabilitation state of the patient can be reflected more comprehensively and meticulously.

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.

Systems and methods for predicting a medical trait in an individual are provided. The techniques include obtaining genomic data including information indicative of genetic variants present in a genome of the individual and calculating, using an at least one processor and a trained computational model, a trait score based on the information indicative of the genetic variants, wherein the trained computational model was obtained by training a first computational model using data describing phenotypes, genotypes, and / or phenotype-genotype relationships for a population including admixed individuals of multiple ancestries. The trait score may be used to alter a course of medical treatment of the individual and / or to implement a clinical trial.

A system and methods for adaptive bandwidth-efficient data encoding comprising: a sequence analyzer configured to analyze a received sequence dataset, maintain a count of unique characters, and identify positions where the unique character count increases by a power of two; an adaptive sourceblock optimizer that determines and dynamically adjusts optimal sourceblock sizes based on dataset characteristics; and a data deconstruction engine that deconstructs the dataset into sourceblocks and creates codewords for storage or transmission. The system analyzes sequence complexity, alphabet size, and character frequency distribution to optimize sourceblock sizes, and uses machine learning to improve decision-making over time. This adaptive approach enhances compression efficiency across varied genomic data types, including genome graphs, while maintaining data integrity and security. The system efficiently encodes, stores, and transmits complex genomic and bioinformatic datasets, addressing the growing challenges in data storage and bandwidth limitations.

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 disclosed technology relates to methods, transcriptomics systems, and non-transitory computer readable media for enabling natural language transcriptomics analysis. In some examples, genomic data including gene expression profiles for cells is transformed into sequences of genes ordered by expression level for each of the cells. The sequences of genes are annotated with metadata in a natural language format. A large language model (LLM) is then fine-tuned using the annotated sequences. The LLM is pretrained for natural language processing (NLP) tasks. The fine-tuned LLM is applied to generate and output a result in response to a received prompt in the natural language format. Thus, the LLMs of this technology advantageously both generate and interpret transcriptomics data and interact in natural language to generate meaningful text from cells and valid genes, among many other types of results.

The invention relates to a method for regulating and controlling gene expression of lactic acid bacteria in shrimp acid fermentation based on systematic biology. The method comprises the following steps: constructing a gene regulation network of lactic acid bacteria by using whole genome data and a high-throughput sequencing technology comprising RNAseq; through clustering analysis and network reconstruction, transcription factors and enzymes of key metabolic pathways of lactic acid generation, acid resistance and glycometabolism are identified, and a dynamic regulation and control model of gene expression is established; a GeneNet or Cytoscape software tool is used, genomics, transcriptomics and metabonomics data of lactic acid bacteria are fused in a multi-layer network, a metabolism regulation and control system is constructed, and the metabolism regulation and control system comprises gene expression regulation and control and real-time change of metabolic flow; the method comprises the following steps: constructing a metabolic flux dynamic model of lactic acid bacteria based on a metabolic flux analysis MFA and a dynamic system modeling technology, and analyzing a mutual relation between a lactic acid generation path and a byproduct generation path; fBA is analyzed through flow equilibrium, and the metabolic network of lactic acid bacteria is optimized.

The invention provides a gastric cancer intestinal microbial marker, application thereof and a method for constructing a gastric cancer detection model, and belongs to the technical field of biomedicine. The method comprises the following steps: extracting biomarkers related to gastric cancer pathogenesis and progress from metagenome data, constructing a random forest model by using a random forest algorithm, and training and testing the constructed random forest model by using a training set and a test set to obtain a gastric cancer detection model. According to the gastric cancer detection model constructed based on the gastric cancer intestinal microbial marker, the specificity is 97.2964%, the sensitivity is 94%, the accuracy is 93.40649%, the model efficiency and conformity are high, and the condition of gastric cancer can be accurately predicted and evaluated. Based on the gastric cancer intestinal microbiological marker and the gastric cancer detection model, the disease condition of a gastric cancer patient can be accurately predicted and evaluated, a doctor can make a more effective treatment scheme, the survival rate and life quality of the patient are improved, and the treatment effect 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.

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 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 a liver cancer patient diagnosis data retrieval precise diagnosis and treatment system based on big data, and relates to the technical field of medical diagnosis. The liver cancer patient diagnosis data retrieval precise diagnosis and treatment system based on big data comprises a case database module, a clinical decision support system, a deep learning module, an early warning visualization module, a data analysis and processing module, a multi-modal data fusion module, a data collection and storage module, a feature selection and selection module and a data acquisition module. Image data, genome data, pathological data and electronic medical record data are integrated through multi-modal data fusion, comprehensive analysis is carried out, the accuracy of risk prediction is improved, treatment schemes are dynamically adjusted according to specific conditions and treatment responses of patients, personalized risk prediction and treatment scheme recommendation are achieved, and the risk prediction and treatment scheme recommendation efficiency is improved by combining gene data. Medicine selection and dosage are optimized, the treatment risk is reduced, the treatment effect is improved, doctors can understand the basis of system decision making, and the credibility of clinical application is improved.

The invention provides a diabetes mellitus intestinal microbial marker, application thereof and a method for constructing a diabetes mellitus detection model, and belongs to the technical field of medicine and biomedicine. The method comprises the following steps: extracting biomarkers related to the pathogenesis and progress of diabetes from metagenome data, constructing a random forest model by using a random forest algorithm, and training and testing the constructed random forest model by using a training set and a test set to obtain a diabetes detection model. According to the diabetes detection model, differential bacteria of a diabetic patient and a healthy control can be identified more accurately, so that the accuracy of early auxiliary diagnosis of diabetes is improved, and the treatment effect is improved. The invention provides specific intestinal microbial markers for identifying diabetes mellitus, which reveal the change in the metabolic pathway of the host-microorganism associated with diabetes mellitus, contributing to the discovery of new biomarkers or therapeutic targets for diabetes mellitus.

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.

Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for compressing genomic data. One of the methods includes obtaining a genomic data read; mapping the read to a plurality of different candidate reference genomes; selecting one of the candidate reference genomes based on the mapping; performing reference-based compression of the read using the selected reference genome; and storing the compressed genomic data read.

The invention discloses a big data-based forest tree planting selection prediction method and system, and relates to the technical field of forest tree planting selection, and the method comprises the following steps: determining a prediction target and a prediction time range, collecting genome data, phenotype data, planting data and growth environment data of a prediction target planting region, and integrating the data into a data set; preprocessing the data set, extracting features related to forest planting varieties in the data set, including uses and key characters, and screening out key features influencing the forest planting varieties through a statistical method; and establishing a prediction model according to the data characteristics and the prediction target. Through application of the big data technology, cross-regional and cross-variety forest tree planting data are integrated and analyzed, key factors influencing forest tree growth, yield and quality are identified, a comprehensive evaluation system is constructed based on the common factors, errors caused by a single standard are reduced, and the accuracy of a prediction result is improved.

The invention provides a method based on Pearson's collinearity selection (PCS). The method is used for screening features closely related to a target phenotype from high-dimensional genome data. According to the PCS method, by calculating Pearson's correlation coefficients among the features, redundant features are effectively removed, the multi-collinearity problem is reduced, the quality of feature selection is remarkably improved, and the stability of model prediction is enhanced. The method is particularly suitable for the fields of genotype and phenotype correlation analysis and the like, and can optimize the data processing and analysis process. In addition, the PCS method has high cross-population adaptability. Feature selection and prediction verification are carried out on data sets of multiple populations, it is proved that multiple models can keep consistent good prediction performance among different data sets by using the PCS method, and the excellent generalization ability is shown. The method not only can effectively predict the phenotype of a single population, but also can be successfully applied to prediction tasks of other populations, and the application range of the method in the fields of precision medicine, crop breeding and the like is further widened. Compared with a traditional feature selection method, the PCS method has the advantages that feature redundancy is reduced, meanwhile, features highly related to the target phenotype can be better reserved, the prediction precision of the model is remarkably improved, and more efficient and reliable technical support is provided for genome data analysis.

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.