7028 results about "Computational biology" patented technology

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.

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 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 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 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 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.

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 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.

The invention provides a topology and biological feature fusion-based virulence gene prediction method, which comprises the following steps of: obtaining a to-be-detected gene; inputting the to-be-detected gene into a trained DAVGAE model, and predicting the correlation degree of the to-be-detected gene and the disease to obtain a disease gene correlation prediction conclusion; wherein the DAVGAE model comprises a data enhancement module, an encoder and an inner product decoder. The problems of data sparsity and heterogeneous data integration in gene-disease association prediction can be effectively solved at least through a DAVGAE model formed by a data enhancement module, an encoder and an inner product decoder.

The invention provides a spatial domain identification method based on data interpolation and cell type deconvolution, and belongs to the technical field of bioinformatics. In order to solve the problems that gap information between adjacent points cannot be utilized in low-resolution spatial transcriptome data and prior information of cell types in a tissue space structure level cannot be fully integrated in a traditional method, the method comprises the following steps: acquiring a spatial transcriptome data set and a single-cell RNA sequencing data set, and performing data preprocessing on the acquired data sets; and carrying out data interpolation on the preprocessed spatial transcriptome data, and carrying out cell type deconvolution in combination with single-cell RNA sequencing data. And constructing a deep learning model based on the graph convolutional network. And training a deep learning model according to gene expression information, spatial position information and cell type information of the spatial transcriptome data after cell type deconvolution by using a self-supervised contrast learning strategy. And performing spatial domain identification on the to-be-detected data based on the trained model.

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 discloses SSR (simple sequence repeat) multiple PCR (polymerase chain reaction) primers for paternity test of grass carp and application of the SSR multiple PCR primers. The primers comprise 16 pairs of specific primers, and base sequences of the 16 pairs of specific primers are respectively shown as SEQ ID NO.1-32 in sequence. The invention further discloses a fish paternity test kit comprising the SSR multiple PCR primer, an SSR multiple fluorescent PCR method for grass carp paternity test and application of the SSR multiple PCR primer, the kit or the method in grass carp paternity test. According to the kit, 16 pairs of specific SSR primers, multiple fluorescent PCR and a universal amplification primer technology are combined, 16 sites can be detected at a time, universal primers can be repeatedly used, and compared with simple single-site detection, the efficiency is improved, and the cost is reduced; the microsatellite loci contained in the invention are 3-6 basic groups, the allele size interpretation is more accurate, and the accuracy of genotype data is improved.

The invention relates to the technical field of Chinese trumpet creeper breeding, and discloses a Chinese trumpet creeper-based efficient breeding decision support system and method. The method comprises the following steps: acquiring multi-dimensional phenotypic data in a Chinese trumpet creeper breeding test, wherein the multi-dimensional phenotypic data comprises plant height, leaf area, flowering period and yield indexes; performing normalization processing on the multi-dimensional phenotypic data, eliminating influences of different dimensions, and calculating a variable coefficient of each phenotypic index; then screening out phenotypic indexes of which the stability is higher than a preset threshold value based on the variable coefficient, and constructing a Chinese trumpet creeper phenotypic feature matrix; carrying out dimensionality reduction on the Chinese trumpet creeper phenotypic feature matrix by adopting an adaptive weighting algorithm, and extracting key phenotypic features; inputting the key phenotypic features into a genetic algorithm optimization module, and calculating the contribution degree of each genetic locus in combination with the Chinese trumpet creeper genotype data; and constructing a Chinese trumpet creeper breeding decision model according to the contribution degree of the gene locus, and outputting an optimal breeding combination scheme. The system can optimize the Chinese trumpet creeper breeding process and provide effective support for Chinese trumpet creeper breeding work.

The invention discloses a mulberry SNP (Single Nucleotide Polymorphism) marker mining and stress resistance character prediction method based on a Transform architecture, and belongs to the crossing field of biotechnology and artificial intelligence. According to the method, mulberry genome sequencing and stress resistance phenotype data are collected, high-credibility SNP is screened through quality filtering and Bayesian statistics, a Transform model containing multiple self-attention mechanisms is constructed after multi-dimensional features are extracted, early stop method training optimization is combined, stress resistance character prediction is achieved, and the key SNP contribution degree is analyzed through feature disturbance. According to the method, the problems of insufficient long-distance feature correlation capture, low prediction precision, poor model interpretability and the like in the traditional technology are effectively solved, the stress resistance character prediction precision and the SNP marker credibility are improved, and an interpretable efficient screening strategy is provided for mulberry molecular breeding.

The invention relates to the technical field of animal monitoring, and provides a wild animal epidemic disease monitoring, prevention and control method and system based on artificial intelligence, and the system collects the video stream, the shell temperature, the air pathogen concentration, the sound characteristics, the VOCs spectrogram and other data of wild animals in real time through arranging multi-mode sensor nodes. Real-time reasoning is carried out through a wildness degree AI model in the edge calculation unit, the health state and epidemic disease risk of animals are evaluated, a multi-source risk knowledge graph and a graph neural network are combined, an epidemic situation occurrence probability threshold value is dynamically adjusted by the system, and accurate prevention and control instructions such as risk area division, isolation early warning and material putting schemes are generated; after the epidemic disease risk is confirmed, the system sends early warning information to a prevention and control center through various communication links, and continuously optimizes a prevention and control strategy through reinforcement learning. According to the method, intelligence and precision of epidemic disease prevention and control of wild animals are achieved, complex ecological environment changes can be coped with in real time, and prevention and control efficiency and accuracy are remarkably improved.

The invention relates to a variation identification method of a nanopore single molecule sequencing current signal, which comprises the following steps of: dividing an original current signal generated by nanopore single molecule sequencing into continuous time periods according to a sequence that a basic group passes through a nanopore, calculating an average signal rhythm, constructing a standard time rhythm when a standard basic group sequence passes through the nanopore, and identifying the variation of the nanopore single molecule sequencing current signal. Comparing the offset difference between the actually measured average signal rhythm and the standard time rhythm, wherein a difference salient region is a potential variation region; current signals of front and back neighborhoods are extracted, a current signal disturbance difference value is quantized, and a region with the disturbance difference value having obvious direction asymmetry characteristics is a variation region to be confirmed; the range is expanded, current signals are extracted, disturbance intensity is calculated, and when the disturbance intensity presents a gradually-enhanced or gradually-weakened continuous change trend, it is judged that the variation area to be confirmed has physical rationality; and determining a theoretical current signal by using the corresponding non-variation sequence, comparing the theoretical current signal with an actually measured current signal, calculating a current difference, and determining that the current difference exceeds a normal difference range as a real variation region.

The invention discloses a black pig breeding disease intelligent monitoring management method based on big data, and relates to the technical field of animal husbandry intelligent management and animal disease monitoring, and the method comprises the following steps: obtaining multi-dimensional data information of pigs in a black pig breeding scene in real time through a sensor network, a video monitoring system and a physiological information collection device; the multi-dimensional data collected in real time is preprocessed and standardized, and the original data quality and the data analysis effectiveness are improved. According to the invention, by introducing an adaptive adjustment mechanism, the problem of pathological data loss caused by excessive elimination of abnormal values in the prior art is solved, and accurate retention and dynamic tracking of early disease signals of pigs are realized. The method integrates multi-dimensional feature extraction and intelligent evaluation, has pathological trend perception and processing strategy adaptive adjustment capabilities, effectively improves the early recognition sensitivity and discrimination accuracy of a disease early warning system, and provides more scientific health management support for farms.

The invention discloses a method for identifying the effectiveness of a Cas target spot by combining a gene large model and ML, and belongs to the field of biotechnology and artificial intelligence. The method comprises the following steps: constructing a detection array of A types of combined sequences and targeted combined sequences, wherein the detection array is provided with A detection units; respectively adding a combined sequence into each detection unit, carrying out incubation reaction, obtaining a fluorescence value of each detection unit, and carrying out normalization processing; carrying out key feature extraction on each combined sequence by adopting a feature extraction model; constructing a label set and a training set, constructing an integrated model, and training by adopting the label set and the training set; forming a machine learning model by the feature extraction model and the trained integrated model; and combining the new target sequence and the PAM sequence into a to-be-analyzed combined sequence, inputting the to-be-analyzed combined sequence into the machine learning model to obtain a predicted fluorescence value, and judging the recognition capability of the Cas protein on the combined sequence based on the fluorescence value. The method can assist in targeted therapy detection.

Provided herein are technologies for framing and evaluating biological sequence-based analysis tasks in a unified, natural-language-based, text in and text out format. Among other things, methods and systems of the present disclosure provide machine-learning technologies for combining biological sequence data, representing, for example, DNA, RNA, and protein sequences, with natural language, conversational style prompts that set out particular analysis tasks to be performed on the biological sequence data. This approach, for example, allows complex analysis tasks, including, but not limited to, identification of various sequence modifications, genes, and regulatory elements in DNA sequences, and quantification of properties such as degradation propensity of RNA and protein stability, to be input to a machine learning model in a uniform text-based format and for output to be generated in a same, unified, text-based format.