8074 results about "Computational biology" patented technology

A federated distributed computational system enables secure biological data analysis and genomic medicine with enhanced oncological therapy capabilities. The system implements patient-specific tumor-on-a-chip analysis through microfluidic control systems and cellular heterogeneity preservation, while integrating fluorescence-enhanced diagnostics using CRISPR-LNP targeting and robotic surgical navigation. The architecture coordinates spatiotemporal analysis of gene therapy delivery through molecular imaging and immune response tracking, and implements bridge RNA integration with multi-target synchronization. Treatment selection is optimized through multi-criteria scoring and patient-specific simulation modeling. Each federated node contains a local processing unit for biological data analysis, privacy preservation protocols, and a hierarchical knowledge graph structure. The system implements cross-species genetic analysis, environmental response modeling, and multi-scale tensor-based data integration, enabling research institutions to collaborate on complex, large-scale biological analyses while maintaining strict data privacy controls.

This disclosure provides methods for spatial profiling of biological analytes present in a biological sample. Methods include generating feature arrays using a master / copy format using recessed arrays, and methods for using such arrays. For example spatially-tagged analyte capture analytes can be used in spatial detection in methods to determine the location of analytes (e.g., proteins) in biological samples.

A database structure obtained by means of information retrieval, and a reasoning system, which structure and system specifically relate to a gut microbe knowledge graph system, comprising: a gut microbe knowledge graph consisting of a gut microbe knowledge base, a gut microbe and small-molecule drug therapy association knowledge base, and a clinical medicine database; and a multimodal uncertainty reasoning system, using the gut microbe knowledge graph. The gut microbe knowledge graph system predicts potential diseases, drugs, genes, etc., which are associated with gut microbes.

A Data Storage Device (DSD) includes a first memory storing reference files used to derive vector embeddings in a vector database. A query vector embedding is received from a host and one or more vector embeddings similar to the query vector embedding are identified in the vector database. One or more reference files from which the one or more vector embeddings were derived are identified and stored in a second memory for faster access. In another aspect, a query vector embedding is received by a host that identifies one or more vector embeddings that are similar to the query vector embedding and retrieves the one or more vector embeddings from a DSD to provide to an Artificial Intelligence (AI) model. One or more reference files are identified from which the one or more vector embeddings were derived and are prefetched from the DSD for storage in a host memory.

The invention relates to the field of microbial communities, and discloses a bioinformatics-based microbial community dynamic monitoring method, which comprises the following steps: acquiring high-frequency acquisition data based on a trace sample, constructing a microbial data stream for time sequence analysis, and carrying out rapid metagenome marker amplification on each sampling unit in the data stream, obtaining a preliminary feature matrix; based on the co-occurrence frequency of the microbial functional genes in the preliminary feature matrix, constructing a multi-dimensional feature mapping graph; performing real-time mode recognition on the flora abundance change trend based on the dynamic fluctuation region; aiming at the key dynamic signal segment, adopting a distributed clustering method based on variation information entropy regulation and control to reconstruct the evolution trajectory of the flora, and generating a flora time sequence behavior vector set; and based on the flora time sequence behavior vector set, performing dynamic alignment with a pre-constructed reference model by using a multi-scale trend matching algorithm. The method has the advantages of high timeliness and automatic processing capability.

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.

The invention discloses a drug resistance prediction method and system based on comparative learning and multi-modal fusion, and the method comprises the steps: firstly generating a molecular map and a molecular fingerprint based on the SMILES of a target drug, and extracting the molecular features of the drug through a comparative learning model constructed through combining a map attention network and a map convolution network; and then, acquiring protein expression, gene expression and metabolic expression data from the target tissue cells, extracting modal features through a deep convolutional network, a Transform encoder and a multi-dimensional attention network, and realizing adaptive fusion of the multi-modal features through a heterogeneous interactive attention mechanism. And finally, jointly inputting the fused multi-modal features and drug molecular features into a multi-layer sensor to realize high-precision prediction of the drug resistance of cells to drugs. By introducing a contrast learning and multi-modal feature fusion mechanism, the characterization capability and prediction precision of the model are effectively improved, and efficient and reliable support can be provided for drug screening and clinical decision making.

The invention provides a multi-modal information fusion feeding decision-making system and method for chicken breeding behavior recognition, and the method comprises the steps: collecting a multi-source heterogeneous data set, and extracting a primary fusion feature vector; constructing a triple knowledge graph; mining implicit association rules of the ingestion frequency and the body temperature; performing secondary fusion on the primary fusion feature vector and an implicit association rule to generate an intermediate decision feature; and generating a feeding decision instruction through the pre-training decision model and the expert rule base. According to the method, the knowledge graph is constructed, GNN reasoning is utilized, and a manual preset rule static mode is replaced; performing secondary feature fusion, generating intermediate decision features by combining primary fusion features and implicit rules, and then combining a pre-training model and an expert rule base, ensuring decision real-time performance, integrating domain knowledge, outputting accurate adjustment parameters, realizing full-link intelligence, improving the accuracy and adaptability of breeding chicken feeding decisions, and improving the accuracy and adaptability of chicken feeding decisions. Therefore, dynamic requirements of complex breeding scenes are met, and chicken flock health and breeding efficiency improvement are promoted.

This disclosure provides a novel method and system for characterizing morphodynamic profiles of objects, such as biological entities. This disclosure provides a shape, appearance, and motion (SAM) phenotype Observation Tool (SPOT). SPOT establishes a standardized SAM “phenome,” image descriptors resembling single-cell transcriptomes, to comprehensively quantify a cell's instantaneous state without prior knowledge. SPOT also establishes a standardized workflow for temporal analysis. SPOT is a generalist tool, applicable to any live-cell imaging and advances biomedical discovery through its standardized, unbiased, streamlined workflow to quantify phenotypic heterogeneity and predict phenotype-genotype-function coupling.

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 semantic sensing analysis system comprising a processor, a memory and at least one sensing element having a plurality of stored semantic routes and / or semantic rules wherein the processor is configured to use semantic factorization to apply a quantifiable factor or indicator based on semantic inference or analysis which is inferred based on at least one of the stored semantic routes and / or semantic rules to cause the system to perform semantic augmentation towards a user in relation with an inferred semantic identity.

The invention discloses a biomedical data analysis method based on adaptive multi-modal data fusion, and belongs to the technical field of multi-modal feature data processing. The method comprises the following steps: acquiring multi-modal data, and preprocessing the multi-modal data; extracting the preprocessed multi-modal data by adopting a modal specific neural network model; performing primary fusion on each extracted modal feature to generate a primary fusion feature; introducing an adaptive fusion module based on an attention mechanism to obtain final fusion features; and performing classification prediction on the final fusion features by using an FCNN model to complete classification of the multi-modal data. According to the invention, through dual mechanisms of primary average fusion and adaptive fusion, high-precision identification is maintained under the condition of multi-modal data missing, and performance reduction caused by incomplete data is avoided; meanwhile, the modal weight is dynamically adjusted through an attention mechanism, the fusion process can be optimized according to the quality and integrity of each modal data, and the feature expression ability is remarkably improved.

The invention relates to the technical field of medical big data analysis and intelligent monitoring, in particular to a medical time series data anomaly detection system which comprises a multi-modal data fusion module used for obtaining a physiological sensor data stream of a target object, performing multi-source heterogeneous synchronization and tensor coding on the physiological sensor data stream, and obtaining a multi-modal data fusion result; constructing a multi-modal physiological time sequence tensor; and the phase-space reconstruction module is used for performing high-dimensional dynamic mapping on the multi-modal physiological time sequence tensor. The one-dimensional time sequence signals are mapped to the high-dimensional Euclidean space through the phase-space reconstruction module, the dynamic manifold structure of the physiological system is restored, abnormity is recognized by detecting the morphological variation of attractor tracks in the high-dimensional space, and even if the physiological parameters do not reach the alarm threshold value in numerical value, the abnormity is recognized. As long as an internal nonlinear dynamic structure is changed, the system can carry out sensitive capture, so that the problem that a traditional system misses detection of early-stage hidden pathological features is effectively solved.

The invention relates to the technical field of social network analysis and influence prediction, and discloses a large-scale social network influence prediction system and method.The large-scale social network influence prediction method comprises the steps that a multi-language knowledge graph alignment system is constructed, and accurate mapping of cross-language concept nodes is achieved; constructing a culture vector space representation system, and extracting culture features from the social network user behavior data; the resonance intensity calculation between the content and the culture vector is realized, and the resonance intensity of the content in a specific culture environment is quantified; realizing culture gene transmission dynamics simulation, decomposing the content into transmissible culture gene units, and simulating the transmission process of the culture gene units; fusing prediction results to realize accurate influence evaluation; the technical problems that an existing social network influence prediction technology is inaccurate in prediction in a cross-language environment and neglects a culture resonance effect and culture dynamics are solved, and more accurate prediction support is provided for applications such as social media marketing and public opinion analysis.

The invention belongs to the field of molecular biological identification, and particularly relates to a specific SNP (Single Nucleotide Polymorphism) site combination for identifying a Wuzhishan pig variety and application. The specific SNP site combination for identifying the Wuzhishan pig variety comprises 77 SNP sites, and the physical positions of the SNP sites are determined by sequence comparison based on a pig reference genome Sscrofa11.1. The specific SNP site combination is screened based on a method of combining whole genome association analysis with selection signal analysis so as to ensure the accuracy of site selection. The selected specific SNP site combination can rapidly realize accurate identification of the Wuzhishan pig variety on the gene level, and has significant application value in the aspects of genetic resource accurate protection and variety utilization of Hainan Wuzhishan pigs.

The invention provides a large model named entity recognition method based on representative sample selection and context enhancement, which comprises a representative sample selection module, an entity knowledge construction module, a dynamic context selection module, a large model calling module and an iterative feedback optimization module, according to representative sample selection, samples with representativeness and information diversity are automatically selected from unlabeled data for labeling through a sample screening strategy based on clustering, entity description integration aims at each entity type, a plurality of high-quality instances are extracted from labeled samples, and standardized entity definition or description prompts are constructed. According to the dynamic context selection, for to-be-recognized text content, a context example most relevant to a target text is dynamically selected from a historical annotation sample or a description set through a semantic similarity retrieval mechanism to serve as auxiliary prompt input, and the adaptability and generalization ability of LLM in a complex or variable scene are improved.

The invention discloses a drug-drug interaction prediction method based on a drug flow sub-graph, and relates to the technical field of bioinformatics, and the method comprises the steps: data preparation: collecting a reference data set including drug-drug interaction, and introducing an external knowledge graph for adaptability preprocessing; constructing a model: constructing a drug-drug interaction prediction model, and training by using the preprocessed reference data set; and effect prediction: inputting a target drug into the drug-drug interaction prediction model, and outputting a drug-drug interaction prediction result through the drug-drug interaction prediction model. The structure and semantic information of the drug flow sub-graph are fully utilized, accurate prediction of drug interaction is realized, and the prediction efficiency is improved. And the interpretability of the drug-drug interaction prediction model is improved.

The invention relates to the technical field of man-machine interaction, in particular to a dialogue Agent interaction method based on multi-modal intention understanding, which comprises the following steps: S1, collecting multi-modal data in a user interaction process in real time, and calculating a time synchronization deviation value of each modal data source; s2, constructing a space-time fusion feature vector; s3, analyzing a dominant action instruction and a recessive behavior clue in the space-time fusion feature vector; s4, generating a multi-level intention analysis tree; s5, when the corrected confidence coefficient of any node in the intention analysis tree is lower than a set threshold value, activating a targeted sensor to complementarily collect data; and S6, analyzing a tree drive response decision according to the finally confirmed intention. According to the method, high-precision identification and response control of the dialogue Agent on the user intention in a complex scene are realized by constructing a multi-modal interaction method with space-time consistency fusion capability, an explicit and implicit intention analysis mechanism and an adaptive modal clarification strategy.

The embodiment of the invention is suitable for the field of environment monitoring of computer vision, and provides a cyanobacterial bloom monitoring method based on multi-modal data fusion and deep learning, in the monitoring method, after obtained initial data is preprocessed, variation point detection is introduced to reduce human factor interference, reduce the calculation amount and enhance the model adaptability; according to the method, a convolutional neural network (CNN) is utilized to extract multi-layer features, the features are input into a long short-term memory (LSTM) network for time sequence modeling, the model performance is improved, on the basis, the features of the CNN and the LSTM are fused, and the model accuracy is further improved; finally, variation point detection is introduced again, rich feature information and multi-mode complementarity are utilized, image key information is enhanced, and detection precision and model robustness are improved. The monitoring method provided by the embodiment of the invention breaks through the limitation of spatial-temporal characteristic splitting of a traditional method, significantly improves the prediction continuity and accuracy, and provides high-reliability support for early warning of cyanobacterial bloom.

The specification provides methods for introducing a desired genetic change in a nucleotide sequence using a double-strand break (DSB)-inducing genome editing system, the method comprising: identifying one or more available cut sites in a nucleotide sequence; analyzing the nucleotide sequence and available cut sites with a computational model to identify the optimal cut site for introducing the desired genetic change into the nucleotide sequence; and contacting the nucleotide sequence with a DSB-inducing genome editing system, thereby introducing the desired genetic change in the nucleotide sequence at the cut site.

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.

The invention discloses a protein active site multi-classification identification method based on multi-modal deep learning. According to the method, protein sequence information, three-dimensional structure information and functional text information are fused, a pre-trained protein language model, an isotropic graph neural network and a biomedical language model are utilized, an innovative multi-modal feature extraction and fusion mechanism is designed, and the model performance is optimized through a self-adaptive weighted fusion strategy. According to the method provided by the invention, accurate prediction of protein active sites can be realized through acquisition of a high-quality data set, construction of a cross-modal feature fusion module, setting of a weighted fusion mechanism and design of a composite loss function.

The invention discloses a wild jujube leaf characteristic fingerprint spectrum construction method and system, and belongs to the technical field of traditional Chinese medicine, and the construction method specifically comprises the following steps: I, collecting original spectrum data of each wild jujube leaf sample, and extracting and fusing deep nonlinear characteristics of each original spectrum data to form a unified spectrum fusion characteristic vector; iI, identifying chemical components in each wild jujube leaf sample, extracting chromatographic peak areas and mass-to-charge ratios of different chemical components, and constructing a chemical component association diagram; the method breaks through the limitation of constructing the wild jujube leaf sample characteristic fingerprint spectrum by traditional single data, can intuitively reflect the synergistic change rule of components of wild jujube leaf samples from different producing areas or in different batches, and enhances the interpretability of the wild jujube leaf sample characteristic fingerprint spectrum at the same time; the distinguishing precision of wild jujube leaf sample differences can be improved, a clear judgment basis is provided for wild jujube leaf sample quality evaluation and active ingredient traceability, and the integrity of the wild jujube leaf sample characteristic fingerprint spectrum is guaranteed.

The invention belongs to the technical field of traditional Chinese medicines, and particularly relates to a traditional Chinese medicine prescription efficacy quantification method and system.The method comprises the steps that chemical component spectrums, pharmacodynamic substance contents and biological activity indexes of traditional Chinese medicines forming a prescription are obtained; constructing a multi-target regulation and control network by combining a traditional Chinese medicine-target interaction database according to the chemical component spectrum, the pharmacodynamic substance content and the biological activity index, identifying a core efficacy pathway cluster by adopting a Louvain algorithm, and calculating a pathway activation intensity value by adopting a pathway target membership weighting model; according to the core efficacy pathway cluster, establishing a mapping relation between the core efficacy pathway cluster and traditional Chinese medicine efficacy terms through ontology and semantic mapping, and according to pathway activation intensity values, generating a prescription efficacy intensity vector through a weighted aggregation model; according to the efficacy intensity vector of the prescription, verifying and correcting the network weight through an in-vitro cell model, and generating the efficacy quantification of the prescription in combination with the clinical syndrome calibration coefficient. Therefore, the problems of single data dimension and insufficient quantification result accuracy in the prior art are solved.

The invention relates to the technical field of bioinformatics, and provides a crop whole genome phenotype prediction method and system fused with an environmental indicator gene, and the method comprises the following steps: collecting re-sequencing data, and carrying out genome comparison to obtain variation site data; performing whole genome association analysis by using the variation site data to obtain phenotype association site information; carrying out gene expression quantity measurement on samples of the crop population material in different environments to obtain gene expression quantity data; performing differential expression analysis on the gene expression quantity data to screen environmental indicator genes to obtain an environmental indicator gene set; constructing a phenotype prediction model of double-branch fusion; and predicting a to-be-predicted material through the phenotype prediction model to obtain phenotype prediction results for different environments. According to the method, environmental factors are incorporated into the whole genome selection model, so that the phenotype prediction precision in different environments is improved.

The invention discloses a grotto temple cavern microenvironment monitoring data quality control and evaluation method, and particularly relates to the field of grotto temple cavern microenvironment monitoring data, and the method comprises the steps: obtaining grotto microenvironment monitoring data, carrying out the time-space reconstruction of the grotto microenvironment monitoring data, and building a data expression basis with a consistent attribute structure. An execution entry is provided for subsequent processing; potential abnormal fragments are identified from the cross relation between the time rhythm and the numerical value change, abnormal points are eliminated, and the validity of the cave microenvironment monitoring data value domain is guaranteed; the problem of disordered timestamp sequence is recognized, a correct sorting structure is recovered in combination with a trend rule, and time sequence consistency is guaranteed. By constructing a multi-stage closed-loop processing chain with rhythm change recognition, numerical mutation elimination, trend sequence recovery and structure quality fusion as the core, structure unification, anomaly recognition, time sequence correction and credibility output of monitoring data are achieved, and therefore effectiveness, consistency and application value of the monitoring data are systematically improved.

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.

This disclosure provides methods for spatial profiling of biological analytes present in a biological sample. Methods include generating feature arrays using a master / copy format using recessed arrays, and methods for using such arrays. For example spatially-tagged analyte capture analytes can be used in spatial detection in methods to determine the location of analytes (e.g., proteins) in biological samples.

The invention discloses an aquaculture disease prediction method based on multi-modal data fusion, and relates to the technical field of aquaculture, and the method mainly comprises the steps: collecting aquaculture data containing structured data and unstructured text data; inputting the structured data into a TabTransform model, carrying out column embedding and modeling of a context relationship between features through a multi-layer Transform encoder, and outputting a structured feature vector; inputting the unstructured text data into a pre-trained BERT encoder, and extracting an output vector marked by the CLS as a text semantic feature; and splicing the structured feature vector and the text semantic feature into a fusion feature, inputting the fusion feature into a full connection layer, and predicting the incidence probability of each target disease through a Sigmoid function. The prediction accuracy is improved, and the problems of single prediction dimension and weak generalization ability in the prior art are effectively solved.