9084results about "Genomics" patented technology

A federated distributed computational system enables secure oncological therapy optimization through robotic integration. The system establishes a distributed graph architecture with secure communication channels connecting computational nodes, implementing encryption protocols for cross-institutional data exchange. Each node contains processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration while maintaining hierarchical knowledge graphs of oncological biomarkers, interventions, and outcomes. The system coordinates domain-specific knowledge through token-space communication and implements an advanced robotic integration system for surgical interventions using spatiotemporal tumor mapping, multi-modal fluorescence imaging, surgical robot coordination, and space-time stabilized mesh management. Key capabilities include wavelength-specific multi-modal fluorescence detection, combined epistemic and aleatoric uncertainty estimation, tensor-based data integration with adaptive dimensionality control, and light cone search for adaptive treatment optimization—all while maintaining strict privacy controls.

The AI-enhanced cellular modeling and simulation platform is a computational system designed to enhance biomedical research and development and personalized medicine and wellness. This platform integrates simulation modeling, machine learning and artificial intelligence, multi-omics data, and sophisticated data fusion and decision-support techniques to create comprehensive models of cellular systems and processes across multiple scales. It enables researchers and clinicians to simulate complex biological interactions, predict disease progression, and design or optimize treatment strategies or medical devices with improved accuracy and efficacy. The system's architecture allows for integration of various components, including real-time data processing, federated learning, and quantum computing enhancements. From personalized drug discovery and cancer therapies to synthetic biology and epidemiological analysis, this platform offers powerful tools for understanding and manipulating cellular systems and bioengineered systems. By bridging the gap between molecular-level interactions between cells and materials and organism-wide effects, it enables significant advancements in healthcare and biological sciences.

A federated distributed computational system enables secure oncological therapy optimization through multi-expert integration and advanced uncertainty quantification. The system implements a multi-expert integration framework that coordinates domain-specific knowledge through token-space communication for precision oncological treatment, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for fluorescence-guided imaging, uncertainty quantification, and expert knowledge integration. Through a distributed graph architecture, the system enables advanced fluorescence imaging with wavelength-specific targeting, multi-level uncertainty estimation combining epistemic and aleatoric approaches, and multi-scale tensor-based integration with adaptive dimensionality control. The system implements light cone search and planning for adaptive treatment strategy optimization, enabling medical institutions and research organizations to collaborate on complex oncological therapy projects while maintaining strict data privacy controls.

A federated distributed computational system enables secure, multi-institutional biological data analysis and genomic medicine through interconnected, decentralized nodes in a federated distributed graph architecture. A federation manager coordinates computational resource allocation, control and data flows, establishes privacy and security boundaries, implements multi-scale spatiotemporal analysis and simulation modeling, models cross-species or intrapopulation elements, and maintains cross-institutional knowledge relationships. Each node includes a local processing unit for biological data analysis, including multiomics and gene editing, privacy-preserving protocols for secure multi-party computation, a hierarchical knowledge graph for managing multi-domain biological relationships across spatial and temporal scales, and encrypted network connections. The system implements cross-species genetic analysis via phylogenetic integration, environmental response modeling through spatiotemporal tracking, and multi-scale tensor-based data integration with adaptive dimensionality control. This architecture enables research institutions to collaborate on complex biological analyses and genomic medicine applications while maintaining strict data privacy and security controls.

The invention relates to the field of medical informatization. The invention discloses a thyroid cancer electronic medical record system based on multi-modal data fusion. The thyroid cancer electronic medical record system comprises a multi-modal data acquisition module which acquires patient texts, ultrasonic images, genes, biochemical indexes and clinical data and performs standardized calibration to generate standard data; the multi-modal feature extraction module extracts semantic, structure, mutation, change and fluctuation features of each standard data through multiple technologies; the single-mode prediction model construction module constructs single-mode prediction models of texts, images and the like based on the features and outputs results; and the multi-modal fusion prediction module fuses the single-modal model based on the deep learning framework to output a multi-modal fusion prediction result. According to the invention, multi-modal data are integrated, and the accuracy and comprehensiveness of thyroid cancer diagnosis are improved. The system ensures consistency through standardized data processing, and assists doctors to accurately judge pathological types, recommend therapeutic schedules and evaluate prognosis by means of a multi-modal feature extraction and fusion mechanism.

The invention provides methods, systems, and computer readable medium for detecting ploidy of chromosome segments or entire chromosomes, for detecting single nucleotide variants and for detecting both ploidy of chromosome segments and single nucleotide variants. In some aspects, the invention provides methods, systems, and computer readable medium for detecting cancer or a chromosomal abnormality in a gestating fetus.

The invention provides methods, systems, and computer readable medium for detecting ploidy of chromosome segments or entire chromosomes, for detecting single nucleotide variants and for detecting both ploidy of chromosome segments and single nucleotide variants. In some aspects, the invention provides methods, systems, and computer readable medium for detecting cancer or a chromosomal abnormality in a gestating fetus.

A federated distributed computational system enables secure biological data analysis and genomic medicine through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates classical numerical simulations with machine learning models for biological system analysis, while maintaining secure cross-institutional data exchange. The architecture coordinates multi-scale spatiotemporal synchronization across computational nodes, with each node containing local processing capabilities for biological data analysis and privacy preservation protocols. The system implements cellular machinery assembly analysis, real-time patient data integration, and multi-modal image integration with spatiotemporal health data annotation. Through a distributed graph architecture, the system enables cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration with adaptive dimensionality control. The system implements real-time therapeutic response prediction through multi-modal data analysis, enabling research institutions to collaborate on complex biological analyses while maintaining strict data privacy controls.

The invention discloses a benign and malignant nodule grading evaluation system based on large model fusion ultrasonic imaging and thyroid gene markers, which can organically fuse non-invasive examination and serological detection, can simulate and diagnose multi-grade risk probability information provided by a gold standard, realizes similar risk grading estimation in a non-invasive mode, and has a wide application prospect. The thyroid nodule risk assessment method can provide visual explanation conforming to clinical logic based on comprehensive information of iconography and molecular biology, can significantly improve the accuracy of thyroid nodule risk assessment, can also effectively improve clinical decision-making efficiency and patient credibility, and has important clinical application prospects. The system comprises a data acquisition module, an ultrasonic image feature extraction module, a gene marker feature extraction module, a multi-modal fusion and hierarchical reasoning module and a generation module.

The invention discloses a genomics-based Parkinson's disease drug target prediction model construction method, and relates to the technical field of drug research and development, and the method comprises the following steps: collecting genomics, transcriptomics and proteomics data related to Parkinson's disease patients, and carrying out quality control and standardization processing; through differential expression analysis and function enrichment, key genes and signal pathways related to Parkinson's disease are identified, and a potential drug target range is determined. By integrating genomics, transcriptomics and proteomics data of patients with Parkinson's disease, molecular mechanisms related to Parkinson's disease can be comprehensively analyzed, multi-target combination is optimized in combination with the ant colony algorithm, the limitation that a traditional single-target model is difficult to capture complex disease network comprehensiveness is effectively overcome, and the method is suitable for popularization and application. The accuracy of target spot prediction is remarkably improved, a more reliable action target spot is provided for drug research and development, and the failure rate of clinical tests is reduced.

The invention relates to the technical field of seed processing analysis, in particular to a big data-based rape seed quality evaluation model, which is characterized in that a data pool is constructed by converging multi-source heterogeneous rape data, and high-quality input is provided for subsequent cross-domain feature association; a cross-domain feature interaction graph is constructed, a graph neural network is used for learning, cross-domain feature interaction is visually presented, and a gene-environment interaction relationship is analyzed; through a four-branch multi-head and cross-modal attention analysis atlas, focusing key features, fusing optimization information and generating a high-quality feature vector, high-order feature nonlinear fusion is realized, and the characterization capability of the model on multi-source heterogeneous data is remarkably improved; an evaluation model is constructed through decoding vectors, and high-yield variety data is extracted, so that efficient screening of rape varieties is facilitated; by deploying the generative adversarial network and transferring the learning adaptive model, the evaluation capability is quickly expanded to a new variety, and breeding intelligence is promoted.

The invention relates to the technical field of breeding planning, in particular to an intelligent breeding planning and decision-making method and system based on a large model. The method comprises the following steps: acquiring a multi-source breeding data set; constructing a structured breeding knowledge graph based on the multi-source breeding data set; performing breeding data association on the structured breeding knowledge graph according to a preset large model to generate a special breeding basic model; obtaining a breeding instruction input by a user; performing user semantic recognition on a breeding instruction input by a user to generate breeding semantic recognition data; inputting the breeding semantic recognition data into a breeding special basic model for breeding intention analysis, and generating user breeding intention data; and determining data information needing to be called based on the breeding intention data of the user, analyzing and screening to generate germplasm resource screening data and a breeding plan / breeding decision scheme. According to the method, the intelligence and operability of breeding planning are improved through integration of multi-source data, intelligent semantic recognition, combined genetic analysis and executable evaluation.

The invention relates to a data processing method and system for multi-source complex biological information data. According to the method, expression profile data, gene variation data and clinical survival data are collected, and unified standardization processing is carried out on the collected data. Feature alignment is performed on different source data based on sample identifiers, a joint feature expression matrix is constructed, and a context dependency relationship across data types is maintained. On the basis, multi-stage feature screening is carried out through Lasso regression and information gain evaluation in sequence, and an optimal feature subset used for modeling is obtained. And further training a risk scoring model by adopting a Cox proportional risk regression method, and calculating a risk scoring value of the sample by utilizing the constructed continuous scoring function. And finally, dividing the score value into a plurality of risk levels, and generating a survival curve of each level in combination with a Kaplan-Meier estimation method so as to verify the risk layering effect and prediction significance of the model. According to the method, the accuracy of biological information modeling can be improved, and the method has good universality and practical value.

The invention discloses an intestinal cancer immune response graph neural network prediction method, a medium and equipment, and the method comprises the steps: collecting pathological image information, immunodetection information and basic clinical information, extracting a tissue space distribution characteristic spectrum through a deep convolutional network, and constructing a graph neural network model in combination with an immunomarker expression characteristic matrix; spatial interaction characteristics of a tumor microenvironment are modeled by adopting a graph attention mechanism, finally a treatment response probability, an optimal treatment opportunity and an adverse reaction risk are predicted through a multi-task learning framework, and a clinical decision report containing a prediction response curve, a risk early warning threshold and a treatment time window suggestion is output. According to the method, through multi-modal data fusion and spatial interaction modeling, accurate prediction of intestinal cancer immunotherapy response is realized, and a more comprehensive reference basis is provided for clinical decision making.

The invention relates to the technical field of database construction, in particular to a construction method and system for a reproductive medicine database. The method comprises the following steps: acquiring a biological tissue sample of a patient; acquiring reproductive endocrine hormone data, embryo morphological parameter data, immune cell function data and genome variation data from the biological tissue sample of the patient; performing time sequence analysis processing on the hormone fluctuation mode according to the reproductive endocrine hormone data to obtain endocrine dynamic characteristic data; performing image recognition processing based on coherent superposition characteristics of light on the embryo morphological parameter data, and performing embryo development potential evaluation to obtain embryo development potential data; and performing hierarchical clustering processing on the individual heterogeneity of the patient according to the immune cell function data and the genome variation data to obtain immune-genetic typing data of the patient. By means of the quantum dot sensing technology and the multi-channel micro-fluidic chip, standardization of data acquisition is achieved, and the heterogeneity problem of a data source is solved.

The invention provides a multi-fusion seaweed field ecosystem observation method, system, device and medium, and belongs to the technical field of ecological monitoring, the method comprises the following steps: obtaining chlorophyll a concentration, seaweed canopy spectrum and three-dimensional biomass point cloud; aligning the chlorophyll a concentration of the target sea area with the seaweed canopy spectrum, and fusing the three-dimensional biomass point cloud to generate a three-dimensional biomass model; constructing an in-situ sampling network to monitor water quality parameters, benthic organism video streams and eDNA metagenome sequencing data, calibrating a three-dimensional biomass model, executing anomaly detection through a lightweight LSTM model, and identifying benthic organism species in real time through an improved YOLOv5 model; constructing a graph neural network, outputting a carbon sink prediction value, generating a brown tide early warning signal when the carbon sink prediction value is lower than a dynamic threshold value, optimizing a patrol path of the unmanned aerial vehicle based on reinforcement learning, and improving the sampling frequency of the water quality sensor. According to the invention, multi-fusion monitoring of the seaweed field is realized, the ecological condition is accurately evaluated, and abnormity is warned in advance.

The invention discloses a microbiological culture scheme customization recommendation method and system based on soil data analysis, and relates to the technical field of microbiological culture plants.The method comprises the steps that soil samples at different positions and depths are collected, soil characteristic data are analyzed, and data processing is carried out; microbial community information is obtained through soil DNA extraction, PCR amplification and high-throughput sequencing, and distribution and functions of microorganisms are evaluated in combination with data preprocessing and diversity analysis; selecting proper microbial strains according to soil characteristics and microbial community analysis results; a microbial culture scheme is combined with agricultural management measures; the conditions of soil and microorganisms are monitored in real time, and a microorganism culture scheme is dynamically adjusted based on a feedback mechanism; through field trials and advanced performance evaluation indexes, the effect of the microbial culture scheme is verified for a long time. The method has high adaptability and sustainability, and customized microorganism management schemes can be provided in different agricultural environments.

The invention provides a method for drug screening and disease model construction of liver organoid, relates to the technical field of medical research, and adopts multi-modal data acquisition, fusion and intelligent analysis and quantum computing processing to optimize comprehensive application of data, so that the richness, accuracy and analysis efficiency of the data are remarkably improved, and the risk of drug screening and disease model construction of liver organoid is reduced. Multi-source data of genomics, transcriptomics and the like are collected comprehensively, an accurate research system of immune-hepatic disease interaction based on organoid is constructed, an in-vivo complex immune-hepatic disease microenvironment can be highly simulated, the proportion and activation state of immune cells are accurately regulated and controlled in an in-vitro model, functional and phenotypic changes of the immune cells are monitored in real time, and the immune-hepatic disease interaction accuracy is improved. According to the present invention, the interaction mechanism between the immune cells and the liver organ cells is deeply revealed, such that the accurate information basis is provided for the accurate determination of the drug action target, the development of the innovative drug for the immune check point or tumor microenvironment regulation is easily achieved, and the pertinence and the success rate of the drug research and development are substantially improved.

The invention relates to the field of data management and knowledge maps, and provides a civil administration data consanguinity knowledge map construction method and device based on a large model, and the method comprises the steps: constructing a basic entity network based on the metadata feature extraction of civil administration data; based on the basic entity network, analyzing the data table structure to generate a data table structure relation model; analyzing the complex structured query language statement, identifying field-level blood relationship, and generating a preliminary blood relationship graph; all physical source tables are traversed, virtual table redundancy and data node repetition are eliminated, and an optimized blood relationship graph is generated; and based on the optimized blood relationship graph, identifying public data nodes and combining field-level data blood relationships to generate a final civil administration data blood relationship graph. According to the method, the problems of difficulty in accurately analyzing complex SQL statements, processing virtual table redundancy and data node repetition in the prior art are solved, and high-precision and automatic civil administration data consanguinity knowledge graph construction is realized.

The invention discloses an intelligent breeding method for commercial crops based on big data analysis, and relates to the technical field of agricultural breeding, the method comprises the following specific steps: seed multi-modal data acquisition and processing: acquiring physical characteristic data of seeds, acquiring physiological indexes and genome information data through biochemical experiments and gene sequencing, and determining the seed multi-modal data according to the physiological indexes and the genome information data; carrying out pretreatment on the raw materials; according to the method, after multi-modal data are integrated and preprocessed, a seed vigor accurate evaluation model is constructed by using deep learning and data fusion technologies, the seed vigor level can be measured more accurately, and meanwhile, the electric signal change of the early growth stage of crops is monitored in real time by using high-precision plant electric signal acquisition equipment, so that the accuracy of the seed vigor evaluation is improved. And a correlation model of the electric signal characteristics and multiple traits of crops is established, and fusion analysis is performed on seed vigor evaluation data and plant electric signal data, so that the breeding efficiency is greatly improved, and powerful support is provided for cultivation of high-quality varieties.

The invention discloses an interpretable machine learning genome prediction method and device, belongs to the technical field of combination of biological breeding, biological information and machine learning, and utilizes an advanced machine learning algorithm to perform parameter optimization in combination with biological prior information. Through processing of multi-source data (genome, transcriptome and epigenetic data), dynamic feature engineering (PCA and PHATE dimensionality reduction) and organic combination of various machine learning models, and an automatic parameter adjustment framework based on a grid search and sparrow search algorithm, genome prediction precision and calculation efficiency are significantly improved; meanwhile, the interpretability of the model is realized based on the SHAP value, the SNP site contribution is quantified, a reference is provided for precise breeding, and the method is suitable for animal and plant molecular breeding and medical genetic analysis, and can accelerate genetic analysis of high-value characters, assist precise breeding decision and disease risk prediction, and promote leap-forward development from experience breeding to intelligent breeding.

This specification describes systems and methods for performing endoscopy, obtaining medical images for inflammatory bowel disease (IBD) and scoring severity of IBD in patients. The methods and systems are configured for using machine learning to determine measurements of various characteristics related to IBD. The methods and systems may also obtain and incorporate electronic health data of patients along with endoscopic data to use for scoring purposes.

Methods and kits are provided for diagnosing of neuropsychiatric syndromes concurrent with SLE (NPSLE) and for determining whether an SLE subject is at risk of developing a neuropsychiatric disease.

The invention discloses a livestock epidemic disease early warning method and system based on big data analysis, and the method comprises the steps: generating a preliminary epidemic situation diffusion path through employing the farm distribution and traffic network data of a geographic information system in combination with a space-time propagation model, and carrying out the dynamic adjustment according to the real-time updated data; predicting a high-risk area by adopting a multi-source data fusion early warning model; the strain mutation risk is judged by analyzing the relevance between pathogen genomes and environmental parameters; deploying a deep learning model at an edge computing node, analyzing animal husbandry behaviors and voiceprint data in real time, and evaluating the animal husbandry health state; and integrating mutation risk analysis and animal husbandry health assessment results to generate a comprehensive epidemic situation early warning report. Through multi-dimensional data analysis and edge intelligent calculation, accurate early warning of animal epidemic situations is realized, and a scientific basis is provided for epidemic situation prevention and control.

The invention discloses a multi-omics pathological analysis system and method for colorectal cancer liver metastasis risk prediction, and relates to the technical field of biomedicine and cancer diagnosis, the system comprises a data acquisition module, a data preprocessing module, a cell analysis module, a score construction module, a model construction and training module and a prediction and evaluation module; according to the method, multiple omics data including the single cell data set, the space transcriptome data, the batch RNA-seq data, the clinical data and the full slice image WSIs are integrated, comprehensive and accurate prediction of the colorectal cancer liver metastasis risk is achieved, cross-dimensional data fusion and analysis improve the prediction accuracy and reliability, and the risk of the colorectal cancer liver metastasis is reduced. The invention also provides a powerful tool for deeply exploring the molecular mechanism of tumorigenesis, development and metastasis, and particularly in a cell analysis module, cell subtypes and molecular characteristics closely related to liver metastasis can be disclosed by identifying and analyzing malignant cell subgroups LMTMCs triggering liver metastasis, so that the liver metastasis can be discovered. And a scientific basis is provided for formulating a personalized treatment scheme.

A method of predicting protein functions and interactions, including identifying a first protein and a second protein for analysis, generating a first embedded representation of a first amino acid sequence of the first protein and a second embedded representation of a second amino acid sequence of the second protein, generating a first contact map prediction for the first protein based on the first amino acid sequence and a second contact map prediction for the second protein based on the second amino acid sequence, generating first physiochemical features associated with the first protein, and second physiochemical features associated with the second protein, and generating a prediction score for a protein function or interaction between the first protein and the second protein based on the first embedded representation, the second embedded representation, the first contact map prediction, the second contact map prediction, the first physiochemical features and the second physiochemical features.

The invention discloses a biological hazard big data analysis and monitoring early warning system, and relates to the technical field of public health safety, and an analysis subsystem in the system comprises a core logic module comprising a quality control unit, an error correction unit, an assembly unit and a box separation unit; the unit analysis module comprises a pathogen analysis unit, a resistance gene unit, a virulence evaluation unit and an evolution development unit; the flora integrated analysis module comprises a traceability analysis unit, a mutation characteristic unit, a propagation evolution unit and a transformation management and control unit; the early warning subsystem comprises a risk assessment and early warning system design unit, a dynamic research and propagation analysis unit, an assessment model construction unit, a pathogen evolution and function research unit, a toxicity and propagation risk comprehensive prediction unit, a pathogen risk monitoring network unit and an unknown pathogen and potential risk identification unit. According to the method, the biological hazard data can be comprehensively, efficiently and accurately analyzed.

Systems and methods described herein include embodiments for generating a geometrical structure of a binding complex formed between a plurality of macromolecules, comprising: processing an input representation comprising a plurality of representations of the plurality of macromolecules to generate a geometry prior; sampling an initial geometrical structure of the binding complex based on the geometry prior; and processing, using a neural network, the initial geometrical structure to generate the geometrical structure of the binding complex formed by the plurality of macromolecules.

The invention provides a genetic disease gene detection data analysis method and system based on big data, and relates to the technical field of gene detection data analys.The genetic disease gene detection data analysis method comprises the steps that quality control and duplicate removal are conducted on gene sequencing original data, sequence comparison and variation detection are conducted, and a standard variation detection report is generated; performing multi-scale feature sampling and optimization by using an improved random field diffusion model, inputting high-dimensional feature distribution into a dual self-activation iterative network for feature extraction and integration to obtain a feature mapping matrix and a feature evolution trajectory, and inputting the feature mapping matrix and the feature evolution trajectory into an integrated predictor to obtain an integrated predictor; and in combination with Gaussian mixture process regression analysis and an improved Bayesian reasoning network, establishing a risk association network and training a deep hierarchical decision tree, and outputting a multi-dimensional risk assessment report.

The invention discloses an intestinal cancer metastasis prediction method and device based on spatial omics, a medium and equipment, and the method comprises the steps: collecting original multi-omics data, and carrying out modal alignment and quality control processing to obtain pre-processed multi-omics data comprising second spatial transcriptome data, second single-cell RNA sequencing data and second pathological image data; performing cross-modal semantic embedding on the second spatial transcriptome data based on the second single-cell RNA sequencing data to generate a spatial enhanced expression profile; performing multi-scale graph construction on the second spatial transcriptome data and the second pathological image data, and extracting spatial heterogeneity features; inputting the spatial enhancement expression spectrum and the spatial heterogeneity features into a pre-trained metastasis risk prediction model, and outputting a liver metastasis probability spatial heat map and a key driving feature list; and finally generating a clinical prediction report containing high-risk area positioning. According to the method, through dynamic optimization of spatial resolution and multi-scale feature collaborative modeling, the sensitivity of early transfer detection is remarkably improved.