4277 results about "Computational biology" patented technology

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.

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.

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 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 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.

Systems and methods for targeted gene modification, targeted insertion, perturbation of gene transcripts, and nucleic acid editing. Novel nucleic acid targeting systems comprise components of CRISPR systems and non-LTR retrotransposon elements.

The invention is applicable to the technical field of clinical medicine, provides a clinical multi-modal cancer drug response prediction method based on feature reconstruction, constructs a clinical multi-modal model for drug response prediction of diffuse large B-cell lymphoma, and aims to predict the drug response of diffuse large B-cell lymphoma by integrating gene sequencing and clinical multi-modal data. And accurate drug reaction prediction is realized. The model adopts an end-to-end multi-stage processing flow: firstly, extracting gene features through TransP-Net, and processing multi-modal clinical data by using a clinical information encoder; then, pseudo-gene features are generated through a clinical-genome filling module to deal with the data missing problem; and finally, multi-modal deep fusion is realized through a clinical information decoder, and a prediction result is output. According to the method, data characteristics and working processes in a real clinical environment are fully considered, two conditions of complete gene data and missing gene data can be processed at the same time, and the method has a good clinical transformation prospect and application value.

The invention provides a stacked cell hierarchy judgment method based on contour concavity and convexity. The method comprises the following steps: performing contour extraction on a cell binary image; calculating a convex hull for the contour point set of each cell, and taking the convex hull as an ideal form reference of the cell in an unshielded state; detecting the convexity defect between the contour point set of each cell and the corresponding convex hull, and quantifying the characteristic parameter of each convexity defect; screening the convexity defects according to the characteristic parameters to determine significant recesses, marking cells with at least one significant recess as concave cells, and marking cells without significant recesses as convex cells; screening candidate cell pairs according to the spatial position relationship between the cells; according to the convex-concave attributes of the two cells in the candidate cell pair and the position relation between the coordinates of the center of mass and the remarkable concave area, the shielding relation between the two cells is judged; and constructing an attribute graph by taking each cell as a node and taking the occlusion relationship as an edge, and generating a cell stacking hierarchy sequence through topological sorting.

The invention provides a method for predicting miRNA-lncRNA-disease ternary correlation through deep tensor decomposition and hypergraph convolution, and relates to the technical field of miRNA-lncRNA-disease ternary correlation prediction. Comprising six steps of integration of multi-source heterogeneous data, generation of three-dimensional tensor representation, hypergraph convolution modeling high-order interaction, graph attention network feature refining, depth graph convolution network enhancement and correlation prediction. Node features in a graph attention self-adaptive refining similarity network are integrated, global structure learning is enhanced by adopting a depth graph convolutional network, and the combination can generate stable and information-rich embedding for final ternary correlation prediction, so that potential complex correlation among various biological entities such as diseases, genes and drugs can be accurately extracted, and the prediction accuracy is improved. The potential relation and mechanism between the biological entities are further disclosed, and comprehensive ternary correlation prediction is achieved.

System and method for hybrid analysis of quantum and classical genetic algorithms is disclosed. The method includes, receiving an input bitstring, the input bitstring being an output of a genetic optimization module, processing the input bitstring to generate quantum processed bitstrings, mutating the input bitstring, mutating the quantum processed bitstrings as a function of the input bitstring and the mutated input bitstring, performing crossover on a combination of the mutated input bitstring and the mutated quantum processed bitstrings, and selecting a set of individuals from the quantum processed bit strings and output of the crossover. The method further includes, determining, after selecting the set of individuals, if the hybrid analysis is complete or incomplete based on predetermined criteria, returning, in response to the hybrid analysis being incomplete, the set of individuals as the input bit string, else, outputting the set of individuals as a result of the hybrid analysis.

The invention provides a method for predicting mRNA translation efficiency and a prediction system. The method comprises the following steps: acquiring mRNA sequence data, adding a classification mark at the starting end of the mRNA sequence data, and processing the mRNA sequence data with the classification mark to obtain an embedded sequence; extracting local features by using a first feature extraction module, extracting global dependency features by using a second feature extraction module, extracting time sequence features by using a third feature extraction module, and extracting external features by using a fourth feature extraction module; modulating the local features by using the global dependency features to obtain modulated local features; and obtaining fusion features based on the modulated local features, the time sequence features and the external features, and performing prediction based on the fusion features to obtain a prediction result of the mRNA translation efficiency. Therefore, the accuracy of predicting the mRNA translation efficiency can be improved.

The invention relates to an electric power multi-modal total element sample fusion labeling method and system based on Transform, and the method comprises the following steps: S1, obtaining multi-modal original data of a production operation site, and carrying out the preprocessing of the multi-modal original data, and obtaining a preprocessed multi-modal data set; s2, according to the preprocessed multi-modal data, multi-modal feature coding and Transform representation modeling are carried out, and through cross-modal feature fusion and information completion, fusion feature representation is obtained; s3, according to the fusion feature representation and a candidate tag set corresponding to the fusion feature representation, obtaining a multi-modal semantic alignment tag set through semantic consistency detection and a semantic mapping mechanism; s4, aligning the label set according to the multi-modal semantics, and constructing a label system structure tree; and S5, based on the fusion feature representation and the label system structure tree, constructing an automatic labeling module to generate a preliminary label, and directly inferring a label based on similar samples and category probabilities. According to the invention, efficient automatic label generation and sample dynamic classification are realized.

The invention discloses a small CRISPR (clustered regularly interspaced short palindromic repeats)-Cas gene editing system and application thereof. According to the invention, based on microbial genome and metagenome data, a class of CRISPR-Cas family protein is mined through a biological information method, and is named as Cas12r. A CRISPR-Cas12r editing tool constructed on the basis of the gene can realize gene editing in prokaryotic or eukaryotic cells. The CRISPR-Cas12r gene editing system obtained by the invention has the characteristics of miniaturization and various PAM types.

The invention discloses a rapid identification and comprehensive evaluation method for a polygonatum cyrtonema provenance, and belongs to the field of improved variety breeding and quality control of traditional Chinese medicinal materials. The method systematically measures a plurality of specific biological traits covering three dimensions of growth, physiology and medicine. And carrying out standardization processing on the measured multi-dimensional data, carrying out dimensionality reduction by using a principal component analysis model, constructing an evaluation model capable of comprehensively reflecting the comprehensive quality of the provenance, and calculating a quantitative comprehensive evaluation score. The score can objectively and scientifically divide different provenances into four different quality grades including excellent, good, common and to-be-improved quality grades. The method has the beneficial effects that the strong positive correlation between the easily measured growth traits of the overground part and the medicinal component content of the underground rhizome for determining the final quality of the medicinal material is established, so that the rapid, predictive and non-destructive early screening of the provenance quality is realized.

The invention discloses a breast cancer recurrence risk prediction method and system based on multi-modal data missing interpolation and gene interpretability enhancement. The method comprises the following steps: firstly, dynamically generating and complementing features of a missing mode by matching a generative adversarial network with a mode missing mask matrix; then, a feature screening mechanism driven by gene information is introduced, through a multi-task learning network, image feature extraction is supervised by using a gene expression tag in a model training process, and image features highly associated with recurrence-related genes are screened out; and finally, fusing the complemented multi-modal time sequence characteristics by adopting Transform, and outputting a recurrence risk probability. According to the method, the robust prediction performance can be realized under the condition of data missing, and meanwhile, image interpretation with a molecular biology basis is provided for the feature screening process of the model, so that the reliability and clinical acceptability of the whole system are enhanced.

The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilizing compound-protein machine learning representations to generate target results. For example, the disclosed systems can utilize a compound-protein interaction machine learning model to generate a compound-protein machine learning representation for compound protein pairs. The disclosed systems can utilize the compound-protein machine learning representation to train and utilize other target machine learning models in generating predicted bioactivity results. For example, the disclosed systems train a target machine learning model from compound-protein machine learning representations to generate ADMET predictions and / or biological perturbation program predictions. Furthermore, the disclosed systems can utilize one or more explainability models in conjunction with target machine learning models trained based on compound-protein machine learning representations to identify proteins that contribute to predicted bioactivity results.

The invention belongs to the technical field of essential gene prediction, and particularly relates to an essential gene prediction method based on DNA large model and time-frequency domain deep learning fusion, and the method comprises the steps: taking a domain DNA large model as a core representation layer, and obtaining special gene representation through cross-species corpus pre-training and task fine tuning; a T-Block and F-Block dual-channel time-frequency fusion structure is adopted, and the local dependence and long-range regulation relation of a gene sequence is synchronously captured by expanding DFT (Discrete Fourier Transform), complex value attention and iDFT (Initial Discrete Fourier Transform) conversion; designing an efficient modeling reasoning scheme of sliding window slices and gene-level aggregation aiming at an ultra-long sequence; in combination with class imbalance and a noise robust training strategy, cross-cell line / cross-platform transferable threshold output is realized through temperature scaling calibration, an uncertainty quantization and structured interface is matched, and drug target screening and experimental design decision are supported. The system supports the realization of multiple programming languages, and can complete low-delay end-to-end reasoning in a conventional hardware environment.

Aiming at the problems of low efficiency, strong subjectivity, insufficient modeling ability, complex hyperspectral data noise, weak waxiness spectrum difference and the like of a traditional rice grain waxiness discrimination method, the invention provides a hyperspectral rice grain waxiness discrimination method based on multi-branch collaborative modeling. The method comprises the following steps: S1, acquiring glutinous and non-glutinous rice grain images by using a hyperspectral imaging system to obtain spectral data; s2, preprocessing spectral data through a combined method of SG smoothing, an asymmetric weighted penalty least square method and multivariate scatter correction to reduce noise and interference; s3, constructing a multi-branch modeling architecture which comprises a CNN local feature extraction module, an SRU spectrum sequence modeling module and a HorNet global high-order modeling module, and outputting a discrimination result through a classifier after multi-branch features are subjected to fusion and pooling attention weighting; and S4, evaluating the performance. According to the method, high-precision lossless discrimination of the waxiness is realized, and a technical support is provided for germplasm screening and quality evaluation in rice breeding.

A genome characterisation system for providing a genome characteristic prediction of a genome of origin associated with an input genomic sequence, the genome characterisation system comprising: an input preparation layer arranged to encode the input genomic sequence in a form suitable for input to a convolutional neural network; a multi-path residual block comprising a plurality of parallel residual routes, each residual route being adapted to receive input data from the input preparation layer and generate residual data corresponding to features of differing length; a self-attention layer arranged to receive residual data from each of the residual routes, generate a set of attention weights based on the residual data and a set of weights, and apply the set of attention weights to the residual data to generate an output tensor comprising data indicative of a relative importance of one or more portions of the input genomic sequence; and an output layer arranged to receive the output tensor from the self-attention layer; and output a likelihood vector indicative of characteristics of the genome of origin.

The invention relates to a chemical production anomaly detection method based on a space-time diagram variational encoder. The method comprises the following steps: selecting public chemical normal working condition data D; an anomaly monitoring model ST-GVAE is constructed, a loss function L (.) is constructed, D serves as input, an Adam optimizer is adopted to train ST-GVAE, model parameters are reversely updated through gradient descent, and trained ST-GVAE'is obtained; a to-be-detected target variable a is selected, normal working condition historical data corresponding to a are input into ST-GVAE ', and an anomaly detection score corresponding to a is obtained and serves as an anomaly detection threshold value of the target variable a; inputting the current chemical working condition data of a into ST-GVAE'to obtain an anomaly detection score corresponding to the current state of a, and if the score is greater than a threshold value, triggering an alarm; and then calculating a reconstruction deviation score B of each node in the PID, taking the node corresponding to the B greater than a deviation threshold as an abnormal node, and outputting the abnormal node. By using the method provided by the invention, the variable abnormal condition of the existing chemical process can be accurately detected and positioned.

The invention discloses a bone infection and drug resistance prediction method and system fusing a knowledge graph and a graph convolutional network. The method comprises the following steps: preprocessing bone infection multi-source heterogeneous data to obtain a standardized data feature matrix; constructing a bone infection knowledge graph based on the matrix and obtaining a knowledge embedding matrix, and fusing the two to generate a medical semantic constraint fusion feature matrix; key medical variables are screened, the maximum information coefficient (MIC) of the key medical variables is calculated, and an adjacent matrix is constructed in combination with medical association strength factors; inputting the fusion feature matrix and the adjacent matrix into a GCN spatial feature extraction module and a BiGRU time sequence dependence capture module to obtain spatial and time sequence features, and fusing the spatial and time sequence features into a space-time fusion feature matrix; and inputting the result into a double-task prediction module, and outputting a bone infection and drug resistance grading probability prediction value. According to the invention, accurate and rapid prediction of bone infection prediction and drug resistance grading can be realized.

The invention discloses a drug relocation model construction method for simultaneously predicting drug-target interaction and drug-disease incidence relation, and belongs to the field of drug research and development, and the method comprises the following steps: integrating heterogeneous networks and attribute characteristics of drugs, targets and diseases, learning multi-relation node embedding by using RGCN, and constructing a drug relocation model for simultaneously predicting drug-target interaction and drug-disease incidence relation; a Gelato algorithm is combined to enhance a network structure, an auto-covariance is introduced to calculate a potential association score, and drug-target interaction and drug-disease association are synchronously predicted; the weighted cross entropy and N-pair loss joint optimization is adopted, unbiased training is realized, the problems of class imbalance and network sparseness are solved, and the model generalization ability and prediction precision are improved.

The invention discloses a thyroid cancer analysis method and device based on real-time fluorescent quantitative PCR. The method comprises the following steps: acquiring to-be-analyzed data; determining sample exclusive threshold line data according to the to-be-analyzed data; calculating a corrected Ct value based on double inflection point dynamics according to the exclusive threshold line data of the sample so as to obtain a final corrected Ct value; and generating a detection result according to the final corrected Ct value. Through optimization processing of the curve fitting algorithm, the linear characteristic of the fluorescence amplification curve is remarkably improved, the interference of background noise on the result is reduced, and the calculation stability of the Ct value is improved.

The invention provides a cell development process dynamic modeling method and device based on time sequence single cell transcriptome data and a medium, and relates to the crossing field of bioinformatics and computational biology. The method comprises the following steps: constructing a Shenchang differential equation learning framework; adjusting parameters of the single cell development state change model based on the Shenxuan differential equation learning framework so as to construct a population cell development state change model; obtaining a cell specific gene regulation network and a population cell gene regulation network based on the population cell development state change model so as to predict occurrence opportunity of cell lineage differentiation and a molecular decision mechanism of cell differentiation; therefore, the problems of incomplete modeling mechanism, insufficient noise processing and lack of energy principle in the existing cell development process are solved.

The invention relates to the technical field of data processing and analysis, in particular to a data interaction method and system based on an intelligent dataset, and the method comprises the steps: capturing an interaction task demand and context through multi-source heterogeneous data fusion and dynamic feature construction, and providing a basis for subsequent processing; a load cloud picture is generated based on cognitive domain division and a graph attention network, and a high-load area and a conflict focus set are recognized in real time; through causal analysis and diffusion coefficient quantification, a conflict propagation path and influence intensity are clarified so as to prevent and cope with conflict diffusion in advance; through combination of load migration trajectory and diffusion coefficient simulation deduction, a conflict generalization trend is predicted, and conflict early warning is realized. Key decision points are positioned through map fusion, and resource allocation is optimized; by triggering adaptive disambiguation and intention reconstruction, contradiction of high-conflict tasks is dynamically reduced, interaction efficiency and accuracy are improved, conflicts are effectively eliminated, and smoothness and stability of data interaction are guaranteed.

The invention relates to a motif search-based enzyme element deep learning mining method and system and application thereof, and the method comprises the following steps: determining at least one conservative motif according to the structure positioning requirement of a target enzyme; scanning in a pre-constructed large-scale protein amino acid sequence local database, and obtaining an amino acid sequence set corresponding to the conservative motif through motif search as a seed protein amino acid sequence set; performing fine adjustment on the pre-trained protein large language model; combining the fine-tuned protein large language model with a reference high-speed framework, performing multiple rounds of iterative mining, and expanding a candidate sequence set in each round by adopting a union set retention strategy and a clustering sampling strategy; and screening and filtering the candidate sequence set obtained by iterative mining by using a conservative motif to obtain a final candidate enzyme sequence to be subjected to experimental verification. Compared with the prior art, the method has the advantages of being capable of achieving both efficient excavation and excavation reliability.

The invention relates to the technical field of molecular biology, and discloses 74 core molecular marker sets for identifying cold resistance of pennisetum alopecuroides, a primer group, a detection reagent, a kit, a gene chip and application thereof. According to the method, core germplasm resources of pennisetum alopecuroides are systematically collected, and the GWAS population is constructed by utilizing multi-environment (different altitudes) conditions, so that wide genetic and phenotypic variation is fully covered, and a representative material foundation is laid for cold-resistant genetic analysis. In the aspect of phenotype evaluation, a method for determining the number of reviving and root cutting seedlings after natural overwintering by combining single-root double-stem-node oblique cutting planting is innovatively provided, a set of scientific and stable cold resistance and reproducibility evaluation system capable of being operated in a standardized mode is established, and the reliability and efficiency of phenotype identification are remarkably improved. The detection is not influenced by human and climate environments; the method realizes efficient and accurate early cold resistance identification of pennisetum alopecuroides, greatly improves the breeding screening efficiency and scale, and accelerates the breeding process.

A complex bait data set construction method based on structure and sequence collaborative redundancy elimination belongs to the field of bioinformatics, and comprises the following steps: screening an initial protein complex structure set, removing entries containing nucleic acids, small molecules or non-protein chains, and selecting binary complexes meeting integrity and resolution requirements; secondly, structure clustering and sequence clustering are carried out based on three-dimensional structure similarity and sequence homology, combined comparison is carried out on the two results, and redundant compound entries which are highly similar in structure and sequence are removed; then, taking each cluster representative compound as a target, generating a plurality of groups of bait structures by using a molecular docking or prediction modeling method, and calculating a quality index; and finally, performing stratified sampling and proportion balance based on the score interval of the quality index, and constructing a high-quality protein complex bait data set with structure and sequence collaborative redundancy elimination and balanced quality distribution. The data set generated by the method has the advantages of low redundancy, high diversity and quality distribution controllability.

The invention discloses a tiny residual focus detection method and system based on ultra-high depth sequencing, and relates to the technical field of tiny residual focus intelligent detection.The tiny residual focus detection method comprises the following steps that on the basis of a sequencing library, splitting is conducted according to a sample index to obtain a to-be-detected sample, and a consensus sequence is obtained according to a molecular identifier of the to-be-detected sample; based on a consensus sequence, filtering out the consensus sequence of which the mass value is less than 25 or the family size is less than 3, and combining a variation type and a distance from a fragment edge as noise introduced into an original nucleic acid molecular chain; a context sequence (context) and a chain direction are used as noise for introducing the capture level of PCR amplification; on the basis of the noise level, the circulating tumor DNA level is estimated in combination with tumor priori knowledge, and the MRD state is determined by detecting the significance of molecular signal sources. According to the invention, the sensitivity and specificity of MRD detection are improved.

The invention discloses a method and system for screening drug targets with space-time specificity and computer equipment, and relates to the technical field of bioinformatics and computational biology. The screening method is based on single cell transcriptome sequencing data, and comprises the following steps: (1) quantitatively reconstructing spatial positioning and functional modes of cells in tissues, namely 1.1) carrying out data preprocessing on the single cell transcriptome sequencing data; 1.2) reconstructing the spatial positioning of the single cell; 1.3) reconstructing a single cell biological function mode; (2) screening a drug target with space-time specificity, wherein the screening comprises the following steps: 2.1) cell-cell communication analysis; the invention discloses a single-cell data analysis method based on a GRN (Gene Regulatory Network), which is characterized by comprising the following steps of (1) establishing a single-cell data analysis method, (2) establishing a GRN (Gene Regulatory Network) taking a specific tissue microenvironment state as a core, and (3) discovering a target spot. The single-cell data analysis method is innovative, provides a new thought and a technical path for research and development of drugs for metabolic diseases and other systemic diseases, and has a popularization and application basis.