5740results 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.

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.

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 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.

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 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.

The invention provides an UTR (Untranslated Region) element H2202 P1-G as well as a construction method and application thereof, and relates to the technical field of mRNA (messenger ribonucleic acid). According to the present invention, the ribosome load prediction and the secondary structure optimization are performed on the natural 5 'UTR of the HIV TAT 202 gene through the BaidleHelix platform, and the obtained HTAT 202 P1 sequence avoids the inhibitory hairpin structure so as to significantly improve the luciferase expression quantity compared to the natural UTR; an ncRNA sequence without a secondary structure is introduced on the basis of the HTAT 202 P1, translation inhibition of a 5 'cap region is further relieved, and the protein expression quantity of the constructed H2202 P1-G mutant (the DNA sequence of the H2202 P1-G is as shown in SEQ NO 1, and the RNA sequence is as shown in SEQ NO 2) is further improved.

Platforms, systems, and methods for genetic generalization in synthetic biology development. According to one aspect, there is provided a method for predicting performance associated with genetic edits, the method comprising: receiving, by a platform, information about a strain of a microorganism, wherein the information about the strain comprises information describing a plurality of genetic edits to a base strain of the microorganism; generating, by the platform, a set of genetic embeddings based on the information about the strain, wherein the generating comprises processing the information about the strain using one or more embedding models, wherein each of the one or more embedding models: receives the information about the strain of the microorganism as input; and applies computational transformations to the input using a corresponding embedding model to generate a multi-dimensional vector representation for each of the plurality of genetic edits.

The invention provides a brain tumor survival prediction method and system based on a multi-modal medical knowledge graph, and belongs to the technical field of brain tumor survival prediction. The multi-modal medical knowledge graph based on third-party knowledge base fusion is constructed; performing feature extraction on the brain tumor multi-modal data; searching an entity corresponding to the brain tumor related data in the multi-modal medical knowledge graph, and converting the entity into feature representation by using an entity representation learning method; the learned feature representation related to the brain tumor type complements the missing data mode, and finally the complemented features are input into a pre-trained survival prediction model to achieve brain tumor survival prediction. According to the multi-modal medical knowledge graph, comprehensive medical knowledge support meeting clinical requirements is provided; the multi-modal mapping knowledge domain is used for missing modal completion of brain tumor survival prediction, and a completion feature is generated by querying an associated entity through the mapping knowledge domain, so that the problem of weak modal missing processing capability in the prior art is solved.

The invention relates to the technical field of biomedicine, in particular to a hepatic cell differentiation degree evaluation method based on multi-omics data, which comprises the steps of sample collection and preprocessing, transcriptomics, proteomics and metabonomics data analysis, multi-omics data integration and modeling and result output. By integrating multi-level biological information, a multi-dimensional scoring model is constructed, the liver cell differentiation state is quantitatively evaluated, and a standardized grading system is provided for clinic. The method can solve the limitation of single omics analysis, improves the evaluation accuracy and reliability, has universality, can be popularized to other malignant tumor research, and assists precise medical development.

The invention relates to an oncogene prediction method based on graph variation self-coding, and belongs to the field of bioinformatics. The method is based on a dual-path neural network framework: a main path processes an original network and features enhanced by a variational auto-encoder (VAE) by using a graph attention network (GAT) so as to capture a complex relationship between nodes; the auxiliary path generates an auxiliary network and features containing global information through an APPNP algorithm, and the auxiliary network and features are aggregated through GraphSAGE to retain structural information. The model introduces jump connection and residual connection to relieve gradient disappearance and enhance feature complementarity. And finally, integrating dual-path information output prediction through a linear layer. The method is verified on a plurality of biological network data sets, the prediction accuracy, robustness and hidden relation recognition capability are remarkably improved, and a reliable tool is provided for cancer research.

A protein complex model interface quality evaluation method based on a multi-scale isovariant graph neural network comprises the following steps: firstly, screening out a co-crystallized natural protein complex structure from a non-redundant protein interaction database PRISM, and generating a bait structure by using a HDock docking algorithm; the method comprises the following steps: firstly, extracting molecular surface interaction fingerprints, atomic-level features and residue-level features on the basis of each compound bait structure, obtaining graph representation of the compound bait structures, then fully capturing and fusing multi-scale information through a depth isotropic graph neural network, and finally obtaining an interface mass fraction through prototype comparison prediction. According to the method, the interface quality evaluation of the protein compound model can be accurately carried out, and the problems of low precision and poor generalization of the interface quality evaluation of the protein compound model are effectively solved.

A federated distributed computational system enables secure drug discovery and resistance tracking through hybrid simulation capabilities. The system implements a hybrid simulation orchestrator that coordinates molecular dynamics simulations with machine learning models for drug discovery 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 molecular dynamics simulation and resistance pattern detection. Through a distributed graph architecture, the system enables real-world clinical data integration, resistance evolution tracking, and multi-scale tensor-based analysis with adaptive dimensionality control. The system implements real-time drug response prediction through multi-modal data analysis, enabling pharmaceutical companies and research institutions to collaborate on complex drug discovery projects while maintaining strict data privacy controls.

Presented herein are systems, methods, and non-transient computer readable media for determining predicted response scores of subjects. A computing system may identify a first feature set for a first subject to be administered with immunotherapy to address a condition. The first feature set may include one or more of: (i) a first radiological feature identified in a tomogram of a section associated with the condition in the first subject, (ii) a first immunohistochemistry (IHC) feature derived from an image of a sample associated with the first subject, and (iii) a first genomic feature obtained from gene sequencing of the first subject for genes associated with the condition. The computing system may apply the first feature set to a model. The computing system may determine, from applying the first feature set to the model, a predicted score identifying a response to the immunotherapy to be administered to the first subject.

The invention belongs to the field of bioinformatics, and relates to an anti-cancer drug response prediction method based on an attention mechanism. The method comprises the following steps: firstly, capturing uniform-dimension drug and cancer cell line characteristics through a multi-layer perceptron; secondly, fusing drug characteristics by adopting a Transform encoder, and constructing a cell encoder for cancer cell line characteristic polymerization; then, designing a cross-modal cross fusion module to promote information interaction between the two; and finally, predicting a semi-suppressed concentration value subjected to logarithmic transformation between the two through a multi-layer perceptron. Experimental results show that compared with an existing optimal method, the method has the advantage that the RMSE is reduced by 2.9%. According to the method, accurate prediction of the anti-cancer drug response is achieved by integrating drug and cancer cell line data, screening of potential anti-cancer drugs can be accelerated, personalized treatment schemes can be optimized, the cure rate of cancer patients is further increased, and the method has great significance in cancer treatment.

The invention provides a construction method of a predictive cell senescence model, a method for predicting the senescence state of a tissue sample based on the senescence model, a method for screening potential therapeutic drugs, equipment, a medium and a program product, and relates to the field of intelligent medical treatment. The model construction method comprises the following steps: acquiring a training set sample expression profile data set; identifying a key senescence gene set from the data set by using a feature selection algorithm; inputting the key senescence gene set into a machine learning model to fit a prediction model, and determining an optimal hyper-parameter to obtain a cell senescence model containing the weight of a single gene in the key senescence gene set; the cell senescence model is a senescence score obtained by calculating the sum of the product of the expression quantity of a single gene and the regression coefficient thereof. The cell senescence model, namely PreCSenM, is constructed by integrating a plurality of senescence characteristic gene sets and a gene scoring algorithm, the accuracy in CS evaluation is superior to that of 10 existing methods, and the application of CS from biological research to clinical scenes is also realized.

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 tetraploid crassostrea gigas parent-child relationship identification method based on SNP sites, and belongs to the technical field of tetraploid crassostrea gigas molecular assisted breeding. The method for identifying the parent-child relationship of tetraploid crassostrea gigas comprises the following steps: (1) sequencing adductor muscles of tetraploid crassostrea gigas to be detected; (2) comparing the reference genome of the crassostrea gigas to obtain the genotype of the specified SNP site of the tetraploid crassostrea gigas; (3) calculating IBS and LOD of the to-be-detected parents and the to-be-detected offspring according to an SNP typing result; and (4) identifying the parent-child relationship between the to-be-detected parent and the filial generation by combining IBS and LOD. The SNP loci have the advantages that 1000 SNP loci for identifying the parent-child relationship of the tetraploid crassostrea gigas are disclosed for the first time, data support is provided for identifying the parent-child relationship of the tetraploid crassostrea gigas, a foundation is laid for developing a liquid phase chip for identifying the parent-child relationship of the tetraploid crassostrea gigas, and the SNP loci have good application prospects.

The invention discloses a protein palmitoyl transferase prediction method and system based on a multi-branch deep convolutional neural network, and belongs to the technical field of bioinformatics and artificial intelligence. The method comprises the following steps: S1, obtaining a to-be-detected protein sequence; s2, inputting the protein sequence into a pre-trained iPalmT model; and S3, judging whether the target protein is palmitoyl transferase or not according to a model output result. The iPalmT model comprises a coding module, two paths of parallel convolution branches, a feature fusion module and a classification module; and after the convolution layers of each convolution branch are stacked, an SE module is arranged and is used for channel weighting and feature re-calibration. The model extracts multi-level sequence features through convolution kernels of different scales, realizes high-precision prediction through feature fusion and a residual structure, can automatically learn multi-scale features from large-scale data, realizes end-to-end palmitoyl transferase recognition, and has high accuracy and good universality.

The present invention relates to the field of tissue-derived cell identification, in particular to a method for identifying tissue-derived cells in the body fluid based on single-cell sequencing technology. In the present invention, on the basis of high-throughput single-cell RNA sequencing data of cell samples obtained from body fluids, by means of reference component analysis (RCA), expression of known tissue-related marker genes, and a tissue-derived cell prediction model constructed based on logistic regression, the specific identification of tissue-derived cells in body fluids is achieved.

The invention discloses an mRNA non-coding region sequence optimization method and system and electronic equipment, and the mRNA non-coding region sequence optimization method comprises the steps: constructing an initial candidate library according to a target protein; inputting the initial candidate library into a pre-trained mRNA sequence optimization model to obtain a prediction data set; performing multi-dimensional scoring and sequence optimization on the prediction data set to obtain a sequence recommendation group; performing biological verification on the sequence recommendation group to obtain an optimized mRNA sequence; wherein the prediction data set comprises a sequence ID, a sequence content, a prediction TE score and a confidence interval. According to the method, the translation efficiency of the mRNA sequence can be efficiently and accurately predicted, the candidate sequence with high expression potential is screened out, meanwhile, the consumption of computing resources is reduced, and the overall design cost is reduced.

The present disclosure provides systems and methods for analyzing a mixed population of cells. In particular, the present disclosure provides systems and methods for digital cytometry of a biological sample, digital analysis of a biological sample, digital purification of a biological sample, evaluation of a disease in an individual, and prediction of a clinical outcome of a disease therapy.

The present invention relates to a computer-implemented multi-module classifier method and system for providing a pathogenicity classification score of a variant of a protein of interest. The classifier comprises a sequence module based on a protein language model (PLM); a structure module based on a graph neural network (GNN); a property module; and a unified head module based on a machine learning model. The invention further relates to methods for preparing, training, and implementing the multi-module classifier system.

An MHCl binding peptide design method based on evolutionary information and a Transform neural network algorithm relates to the field of protein design, and comprises the following steps: S1, extracting evolutionary information features of alleles of MHCII molecules and binding core sequences of binding peptides corresponding to the alleles, S2, establishing a neural network model based on fusion of a convolution module and a Transform module, and S3, establishing a neural network model based on fusion of the convolution module and the Transform module, the method comprises the following steps: S1, extracting two frequency characteristic tensors from S11 and S12, taking the two frequency characteristic tensors extracted in S11 and S12 as double inputs, and finally obtaining probability distribution of 20 amino acids at each position of each sequence, and S3, according to an output result of a neural network model, carrying out random sampling according to the probability, and generating a binding core sequence of MHCII-peptide meeting target distribution. According to the method, evolutionary information such as sequence position amino acid frequency (first-order conservative analysis) and combined frequency (second-order conservative analysis) of amino acid pairs is introduced to design a new short peptide sequence, the problem that short peptides cannot be designed based on structures is solved, and the reliability of short peptide sequence design based on evolutionary information is provided.

The invention belongs to the technical field of antibacterial peptide activity identification and evaluation, and relates to an antibacterial peptide activity and MIC value combined prediction framework based on cross-modal deep learning, and the framework uses a protein language model ESM2 to respectively carry out token-level semantic embedding coding on an antibacterial peptide sequence and a pathogen protein sequence; performing cross-modal feature extraction and fusion through a multi-branch structure comprising a Mama module, a multi-head self-attention mechanism and DASM 1D convolution; a multi-task decoding structure is adopted to realize antibacterial peptide activity classification and MIC value regression prediction at the same time; according to the method, functional characteristics in the sequence can be effectively mined, the accuracy and generalization ability of antibacterial peptide activity and MIC value prediction are remarkably improved, and a reliable calculation tool is provided for high-throughput screening and rational design of the antibacterial peptide.

The invention provides a cell specific transcription factor regulatory network analysis method and a visualization platform, and relates to the technical field of bioinformatics, the method comprises the following steps: constructing a gene regulatory network through a GRNBoost2-cisTarget-AUCell-Cell GRN workflow based on a transcription factor in combination with a motif database; screening a direct regulation relationship in combination with the database, and calculating an activity score of a regulator in each cell; based on activity scores and cell type annotation results, grouping the single cell data by using a unified manifold approximation and projection (UMAP) dimensionality reduction method and a Leiden clustering algorithm, and displaying the following results through an interactive visualization tool: a cell clustering UMAP graph, performing color marking according to cell types; a UMAP graph and a heat map of transcription factor regulator activity; according to the visual map of the gene regulation and control network, transcription factors and target genes are distinguished through node shapes, and regulation and control relations are marked through line weights and colors. According to the invention, an accurate regulation and control network can be provided.

A crop genotype-to-phenotype general prediction method based on multi-task learning comprises the following steps: collecting genotype and phenotype data sets of five crops, performing quality control and coding on genotype data, and performing standardization processing on phenotype data; dividing the data set into a training set and a test set in proportion by adopting a stratified sampling method; by taking genotype data as input and phenotype data as output, a universal prediction model based on a multi-task learning framework is constructed, information sharing among different phenotype prediction tasks is realized, and potential relations between complex phenotypes and genotypes are accurately captured. According to the method, phenotypes can be accurately predicted according to genotypes in the early stage of breeding, so that materials with target characters are rapidly screened, the breeding period is effectively shortened, the breeding efficiency is remarkably improved, and an efficient and universal technical scheme is provided for crop breeding and related research.

Methods and systems for performing a clinical prediction are provided. In one example, the method comprises: receiving first molecular data of a patient, the first molecular data including at least gene expressions of the patient; receiving first biopsy image data of the patient; processing, using a machine learning model, the first molecular data and the first biopsy image data to perform a clinical prediction of the patient's response to a treatment, wherein the machine learning model is generated or updated based on second molecular data including at least gene expressions and second biopsy image data of a plurality of patients; and generating an output of the clinical prediction.

The invention discloses a crested ibis habitat ecological quality trend analysis method and system, and relates to ecological quality monitoring. The method comprises the following steps: S1, constructing an intelligent sensing network, synchronously obtaining multi-source data of a habitat, identifying activity events of crested ibis, and generating a multi-dimensional habitat parameter table; s2, collecting environmental samples, and generating a microbial functional gene abundance matrix through metagenome sequencing and bioinformatics analysis; s3, taking the activity events of the crested ibis as behavior tags, and generating habitat function health indexes by coupling the parameter table and the matrix training machine learning prediction model; s4, performing spatial interpolation and trend analysis based on the habitat function health index to generate an ecological quality space-time evolution graph; and S5, based on the ecological quality space-time evolution graph, performing quantitative analysis by using a spatial differentiation statistical model, and generating a trend analysis report. By fusing multi-source data, real-time dynamic evaluation of habitat ecological quality and quantitative analysis of driving factors are realized, and a direct decision basis is provided for accurate protection.

The invention discloses a bidirectional reversible conversion method and system between a peptide molecule SMILES and a sequence expression. The core innovation lies in that a new sequence description syntax is defined to retain information of a polypeptide special bond and specific modification of amino acid; a main chain atom index and adjacency traversal topology identification algorithm is adopted, and end group and topology integrated detection and coding are carried out; a residue recognition algorithm for main chain cutting and template library matching is compatible with any standard or non-standard amino acid residues, an extensible end group library / monomer template library and an automatic increment mechanism, and automatic recognition and sequence annotation of S-S disulfide bonds; the invention relates to a high-fidelity assembly algorithm of HELM anchor points and topology aware cyclic peptide processing. The method solves the problems of incapability of supporting a complex polypeptide topological structure, poor reversibility, insufficient expansibility of a monomer library and the like in the prior art, can be widely applied to scenes of quantitative structure-activity relationship model construction, large-scale polypeptide data cleaning and the like, and has remarkable practicability and innovativeness.