158 results about "Bio informatics" patented technology

This invention relates to the fields of genomics, molecular biology, bioinformatics, and genome-wide selection breeding, specifically a 20k liquid-phase breeding chip for the whole genome of *S. esculenta* and its applications. The liquid-phase chip contains background SNPs and functional SNPs located on the *S. esculenta* reference genome; wherein the background SNPs are uniformly distributed within the genome; and the functional SNPs are associated with important economic traits of *S. esculenta*; these important economic traits include one or more of the following: saponin content, polysaccharide content, and heat tolerance. The chip can be applied to the assessment of genetic diversity in *S. esculenta*, identification of germplasm resources and phylogenetic relationships, genome-wide association analysis of important economic traits, and genome-wide selection breeding. This chip has advantages such as high throughput, high region coverage, high locus detection rate, and high flexibility, providing powerful tool support for molecular breeding of *S. esculenta*.

This invention belongs to the field of bioinformatics and relates to a single-cell trajectory inference method based on adaptive feature selection. First, an initial gene expression matrix is ​​obtained through data preprocessing and screening for highly variable genes. Second, a two-dimensional evaluation strategy is employed to calculate the scores of highly variable genes with gene expression variability and the trajectory importance score related to differentiation trajectories. Then, a dynamic weight fusion mechanism is introduced, adaptively adjusting the fusion weights of the two scores based on performance feedback, and highlighting key genes through nonlinear enhancement. Next, an intelligent inflection point detection algorithm adaptively determines the optimal number of features. Finally, trajectory inference is performed based on a variational autoencoder model reconstructed from feature subsets, and a performance-driven feature selection closed loop is formed through multiple rounds of iterative optimization. This invention achieves high-precision, adaptive single-cell trajectory inference, solving the technical problems of single feature selection and fixed weights in traditional methods.

A method for predicting vertigo targets using membrane protein-specific weighted interaction enhancement belongs to the interdisciplinary field of bioinformatics and machine learning. This invention uses vertigo membrane protein targets as the core adaptation object, constructing a complete technical chain of "membrane protein-specific multi-dimensional feature input → MRMR feature screening → membrane protein-specific feature correlation weighting – cross-view interaction enhancement fusion → normalization processing → SVM classification prediction." All parameters are fixed and optimized throughout the process, requiring no adjustment based on test data. Through specific embodiment testing and multi-dimensional verification, it is proven that the method's prediction results are realistic, effective, and stable. The prediction accuracy and efficiency are significantly better than existing mainstream methods, and it is currently one of the few dedicated prediction methods specifically designed for vertigo membrane protein targets, filling a gap in existing technology.

This invention relates to the field of bioinformatics, and particularly to a method and system for denoising single-cell immune repertoire sequencing data. The method includes data preprocessing and feature extraction, bidirectional collaborative denoising, intelligent comprehensive judgment and classification, data archiving and background learning, and result output. Compared to existing technologies that primarily rely on static thresholds for cell filtering, which struggle to comprehensively assess and eliminate multi-dimensional noise, leading to incomplete purification and the potential deletion of high-value cell information, this invention employs a systematic denoising scheme integrating multi-parameter dynamic threshold filtering, specific gene contamination analysis, and targeted optimization of VDJ data. It sets dynamic thresholds by integrating multi-dimensional quality control indicators such as UMI number, gene number, and the proportion of mitochondrial and ribosomal genes, and specifically identifies and filters interfering genes and background sequences. This enables refined and hierarchical removal of complex noise, significantly improving the overall quality of cell datasets and the accuracy of VDJ receptor sequence analysis.

This invention belongs to the fields of molecular biology and bioinformatics, specifically relating to a truncated bovine virus-encoded protein phage library, its construction method, and applications. This library covers all 162 known bovine viruses that host bovine subfamily viruses, containing 51,794 non-redundant peptide coding sequences of 56 amino acids each. These peptides are fused and displayed on the surface of the T7 phage capsid, with all coding sequences having a uniform total length of 200 bp. The library construction was achieved through sequence acquisition, truncation and redundancy removal, codon optimization and DNA synthesis, and in vitro ligation and packaging with T7 phage. The resulting library exhibits a positivity rate of 93% and a titer of 2.8 × 10⁻⁶. 10 PFU / mL. This invention features an optimized single-round high-throughput screening process adapted to clinical bovine serum samples, enabling efficient identification of bovine virus B-cell epitopes and providing tools and candidate targets for bovine virus vaccine development and diagnostic reagent development.

This invention relates to the field of bioinformatics, and particularly to an artificial intelligence-based drug relocation method, apparatus, device, and storage medium. The AI-based drug relocation method of this invention first constructs a heterogeneous network, and then builds a relocation model including a multi-relational heterogeneous graph encoder, a multi-head attention mechanism module, and an interactive decoder. Based on the heterogeneous network, it predicts the treatment score of a drug for a disease, achieving better test metrics compared to other methods. Furthermore, this invention interprets the prediction results, identifying key functional nodes and explaining the pharmacological mechanism of drug treatment for diseases.

The present application relates to the technical field of bioinformatics and artificial intelligence, in particular to a virtual cell construction method and system, the method comprising: taking single cell gene expression matrix and perturbation condition data as input data; constructing an encoding network based on a structural causal model to obtain latent representation, and constructing a perturbation variable according to the perturbation condition data, modeling the latent representation and the perturbation variable to obtain decoupled latent representation; constructing a continuous time evolution path from an initial distribution to a target distribution based on a flow matching model, and determining state changes according to the decoupled latent representation; numerically solving the continuous time evolution path to output virtual cell expression data. The present application is used to solve the problems of causal aliasing, insufficient distribution out-of-distribution generalization ability, unstable generation process and difficulty in counterfactual reasoning in the existing single cell perturbation prediction method.

This invention provides a Slurm-based bioinformatics workflow management system. The module management subsystem stores bioinformatics software commands in a database according to a predetermined format, defining them as modules. These modules can be combined into new modules through serialization or network connections, enabling the rapid construction of complex workflows. The task submission system converts these modules into corresponding task scripts. These scripts are generated in a nested manner, consisting of a main job, sub-jobs, and the smallest job unit. The scripts conform to the Slurm job scheduling system specifications and include a progress report. This workflow management system, combined with front-end and back-end technologies, enables visual interaction, improves the management and utilization efficiency of bioinformatics software commands, and promotes the rapid development of life sciences.

The application relates to the technical field of artificial intelligence and bioinformatics, in particular to a multi-source spatial multi-omics data integration method based on hierarchical graph contrast learning, which comprises the following steps: preprocessing acquired multi-source spatial multi-omics data, and constructing a spatial adjacency graph based on spatial distance; using a graph attention autoencoder to encode modality-specific features of the preprocessed multi-omics data based on the spatial adjacency graph, so as to obtain latent feature representation and negative representation of each spatial node; integrating the encoded latent feature representation and negative representation through hierarchical graph contrast learning; decoding and reconstructing the integrated features based on a neural network, and outputting reconstructed features for downstream tasks to minimize reconstruction error. The method can improve the full-scene data integration capability and improve the multi-omics data fusion precision.

The application belongs to the technical field of bioinformatics, and discloses a multi-modal cell deconvolution method and system based on priori driving. The method comprises the following steps: uniformly naming, deduplicating and intersecting filtering genes / proteins of reference single cell data and target data, and performing scale standardization in respective data domains; generating a pseudo bulk training set based on the scale-standardized reference single cell data; mapping sample expression to a pathway space based on a GMT file of an external knowledge base, the pseudo bulk training set and the target data; constructing a deconvolution model, and respectively performing forward processing on double-view data of a source domain or a target domain by using a double-branch encoder with shared weights, so as to complete cell deconvolution.

This invention discloses a method for designing transmembrane regulatory proteins based on cueing strategies and generative models, belonging to the field of bioinformatics. It includes a target survey stage and a closed-loop design stage. In the target survey stage, a generative diffusion model is used to generate virtual probes targeting the membrane protein. A set of complex conformations is obtained by combining sequence design and structure prediction models, and binding hotspot regions are identified based on the spatial distribution density of the probes, overcoming the dependence on manually specified binding sites. In the closed-loop design stage, based on the identified hotspot regions, an initial backbone is generated using a generative diffusion model. A structure cueing strategy guides the sequence design and structure prediction models to perform closed-loop iterative optimization, generating sequences that selectively bind to the transmembrane domains of membrane proteins and regulate their functions. This invention achieves automated, function-guided design of transmembrane regulatory proteins, effectively expanding the range of designable targets and significantly improving the stability and functional specificity of the designed products in the membrane environment.

A protein structure-density map fitting method based on feature point matching belongs to the field of bioinformatics and structural biology, constructs a protein complex structure dataset, converts the structure into a unified resolution point set through voxelization and uniform sampling, and adopts the farthest point sampling to construct a multi-resolution point set to depict scale geometry; a deep learning network is adopted to learn the rotation equivariant and invariant features of the structure and the density map under multi-resolution, and a geometric self-attention mechanism based on nearest neighbor layer-by-layer expansion is introduced at the neck to enhance the representation; a coarse-to-fine feature point matching strategy is adopted in the training stage, combined with a superpoint matching loss, a point-level matching loss and a contrast rotation loss optimization; in the inference stage, multi-scale and hybrid sampling are adopted, and candidate poses are generated through translation mask, the candidate poses are screened and optimized according to the structure-density map fitting score, and the final fitting result is output. The present application can realize high-precision and high-efficiency structure-density map fitting under complex conditions.

The present application relates to the field of genetic engineering and bioinformatics, and particularly relates to a method for obtaining glyphosate-resistant trait without fitness cost. The method comprises the following steps: generating at least two copies of endogenous epsps gene in a plant by chromosome fragment doubling, wherein at least one copy of the epsps gene is subjected to a point mutation for glyphosate resistance and at least one copy remains wild type. The plant obtained by the method has no obvious loss of agronomic traits compared with wild type plants, and has significantly improved resistance to glyphosate herbicide, and has significant application value.

A protein active site prediction method based on geometric graph neural network belongs to the field of bioinformatics and protein structure analysis. First, the original protein data is preprocessed by multi-modal feature extraction and geometric graph construction, and ProtT5 deep embedding and physicochemical properties are fused. Then, a deep ActiveSiteGNN model with explicit geometric perception ability is constructed, and the stacked geometric encoder and geometric edge update layer are used to dynamically capture the micro three-dimensional spatial features. Next, a multi-task collaborative optimization and dynamic threshold search strategy is designed, combined with weighted sampling to solve the serious sample imbalance, and the best decision threshold is selected based on the validation set in real time. The integrated reasoning and graph diffusion smoothing technology is introduced to globally calibrate the prediction probability distribution based on the biological space prior. Finally, the evaluation is carried out on the independent test set. The method has strong structure perception ability and provides a feasible solution for accurate prediction of protein functional sites.

The application belongs to the technical field of bioinformatics, and discloses a protein disulfide bond mutation site prediction method and device, equipment and a storage medium. The predicted structure of a to-be-transformed protein is acquired to extract a plurality of candidate residue pairs, and the structural feature information of each candidate residue pair is extracted, including the inter-residue distance matrix of the two residues, the sequence distance, and the relative solvent accessible surface area, depth index and predicted local distance difference test value of each residue. The predicted model is input to obtain the probability value of each candidate residue pair forming a disulfide bond. The candidate residue pair with a probability greater than a specified probability is determined as a disulfide bond mutation site. Therefore, more comprehensive features can be used to comprehensively predict the bonding probability of the disulfide bond, the model is less dependent on individual traditional features, the high sensitivity of the traditional method to atomic coordinates and the uncertainty of the prediction result are overcome to a certain extent, the robustness is higher, and the prediction accuracy of the protein disulfide bond mutation site is improved.

The application discloses a diabetes patient biological age calculation method based on glycosylation markers and a multi-task deep learning framework, and belongs to the technical field of bioinformatics and medical data processing technology, and the method comprises the following steps: obtaining glycomics feature data and clinical examination data of diabetes patients; preprocessing the data; obtaining a feature set by adopting a multi-method screening and fusion strategy; constructing a deep learning model comprising a multi-modal feature fusion module and a multi-task learning module for training, wherein the multi-task learning module comprises a main task of aging clock regression and at least one auxiliary task; and outputting biological age prediction values and age acceleration evaluation results of individuals by using the trained model. The method realizes deep fusion of multi-modal biological data and exclusive modeling for the diabetes population, can accurately quantify the biological aging process and the deviation degree relative to the calendar age, and provides an objective quantitative tool for clinical evaluation.

This invention discloses a method for screening pancreatic lipase inhibitory peptides based on machine learning, relating to the fields of bioinformatics and food science. The method includes the following steps: dataset construction and three-dimensional peptide structure characterization, statistical-based feature screening, outlier removal based on a dual-model consensus mechanism, feature recursive elimination and physicochemical significance optimization, data augmentation based on Gaussian noise injection, and final prediction model construction. This invention effectively solves the problems of insufficient feature representation, high noise interference, and weak model generalization ability in existing screening methods through multi-dimensional feature characterization, rigorous outlier cleaning, and data augmentation strategies for small samples. It can quickly and accurately screen highly active pancreatic lipase inhibitory peptides from massive peptide sequences, providing an efficient computational tool for the development of functional foods and lipid-lowering drugs.

This application discloses a combination of plasma protein biomarkers for predicting healthy longevity and a screening method thereof, relating to the field of bioinformatics. The method includes: acquiring genetic association data between plasma proteins and the healthy longevity phenotype; extracting single nucleotide polymorphisms (SNPs) associated with plasma protein levels as instrumental variables; using Mendelian randomization to identify plasma proteins causally associated with the healthy longevity phenotype and performing sensitivity analysis; performing mediation analysis on the causally associated plasma proteins to identify plasma proteins that influence the healthy longevity phenotype through health-related factors; and constructing a combination of plasma protein biomarkers from the identified plasma proteins. This application screens proteins causally associated with healthy longevity and mediated by health-related factors from genetic association data to construct biomarker combinations, solving the technical problem of low prediction accuracy caused by prior art relying solely on correlation analysis, and achieving the technical effect of improving the prediction accuracy of biomarkers.

This invention belongs to the field of agricultural biotechnology, specifically relating to locusts. LARP4B Genes, their dsRNAs, and applications. To provide novel target genes for green pest control, this invention utilizes bioinformatics methods to obtain genes from the transcriptome of locusts. LAPR4B The gene fragment was further cloned and sequenced, yielding the sequence SEQ ID NO: 1. Based on SEQ ID NO: 1, the dsRNA of this gene was designed and synthesized. The nucleotide sequence of this gene fragment is shown in SEQ ID NO: 2. Injecting it into the locust's body cavity specifically silences the target gene, inhibiting the development of the locust's ovary, resulting in a smaller ovary and shorter, narrower oviducts, thereby effectively suppressing the locust population. Due to the specificity of this invention and its effect on ovarian development, it has significant practical implications for suppressing locust populations and can provide a new approach for pest control.

This invention discloses a method and system for single-cell transcriptome cell type annotation based on deep learning, belonging to the field of bioinformatics data processing technology. The method includes five steps: data quality control preprocessing, Transformer cell encoder pre-training, graph attention network cell relationship modeling, hierarchical classification annotation, and zero-shot transfer annotation. This invention deeply couples Transformer representation learning with graph attention networks, obtains general representations through masked gene prediction pre-training, enhances rare cell type features using KNN graphs and graph attention message passing, improves recognition accuracy by adopting a hierarchical classification architecture from main lineage to subtype, and supports zero-shot cross-modal annotation based on text description.

The present application relates to the technical field of data processing, in particular to a kind of spectrum radius feature extraction and Hamiltonity determination method for mixed graph structure.It includes the following steps: obtaining mixed graph data and constructing adjacency matrix, Laplacian matrix and unsigned Laplacian matrix;Three spectrum radii are extracted using power iteration acceleration algorithm, and a spectrum radius pyramid is constructed by multi-layer coarsening, forming a multi-scale spectrum feature vector;The feature vector is input into the classification model to output the Hamiltonity probability value;The confidence score is calculated by multiple random dropout, and it is dynamically decided whether to activate the accurate determination engine;Finally, the determination result is output.The present application combines multi-scale spectrum features and statistical features, uses graph neural networks to achieve high-precision probability prediction, and improves the reliability of boundary samples through confidence evaluation and cascade determination architecture, suitable for graph data analysis in the fields of communication networks and bioinformatics.

This invention relates to a method for disease therapeutic target discovery and drug prediction based on multi-omics networks and deep learning models, belonging to the interdisciplinary field of bioinformatics and artificial intelligence drug discovery. The method includes: integrating genomic expression profiles and common molecular interaction data from disease and control groups to construct a candidate whole-genome network; refining the network based on expression profile data through systematic modeling and the AIC criterion to obtain the real molecular interaction network; extracting the core network using the master network projection method and identifying key targets through pathway analysis; predicting candidate drugs interacting with the targets using a pre-trained deep neural network model; and finally screening potential therapeutic drugs based on multi-dimensional criteria such as regulatory ability, sensitivity, and toxicity. This invention achieves a complete integration from disease mechanism analysis to drug prediction, and is particularly suitable for complex diseases such as atopic dermatitis. It can systematically discover precise targets and efficiently predict repositionable drugs, significantly improving R&D efficiency.

This invention discloses a tobacco-derived α-glucan phosphorylase, its mutants, and their applications. Addressing the problems of low catalytic efficiency, poor stability and adaptability, and difficulty in heterologous expression of key enzyme elements in cellulose-to-starch synthesis, this invention utilizes metagenomics and big data analysis of public databases, along with bioinformatics methods, to precisely identify novel key enzyme elements for cellulose degradation and starch synthesis. An α-glucan phosphorylase from tobacco was identified and found to have better enzyme activity and starch synthesis function than the potato-derived PGP. Furthermore, this invention involves a single point mutation of the Cap domain of the α-glucan phosphorylase. The results show that the single point mutant obtained by mutating the tobacco-derived α-glucan phosphorylase with the amino acid sequence shown in SEQ ID No. 1 by the S524H single point mutation has significantly higher enzyme activity and expression level than the wild-type α-glucan phosphorylase.

The present application relates to a kind of metabolic pathway extension network based on structural feature automatical establishment method, belong to bioinformatics and computational chemistry technical field.The purpose is to solve the problem of existing KEGG database incomplete, annotation lag of metabolite.Method includes: obtaining KEGG skeleton and modified group construction seed library;Through substructure matching and set difference calculation to determine the attribution of derivative;Iterative expansion forms structure association set;Combining stereochemistry rule and mass difference verification screening derivative;Merging KEGG reaction and extended relationship constructs network, and remove redundant path by graph theory method, finally output extended pathway network.The method greatly improves the annotation rate of metabolite pathway, supports in-depth functional analysis of metabolomics data.

The application discloses a large-scale mass spectrometry queue-oriented joint alignment and quantification method, relates to the cross field of bioinformatics, artificial intelligence and high-performance computing, and outputs a quantitative feature matrix through an end-to-end deep learning network; wherein, in a unified back propagation process, the end-to-end deep learning network synchronously optimizes the retention time alignment, the cross-batch intensity drift correction and the quantitative regression based on a joint loss function, the application regards the retention time alignment, the cross-batch intensity drift correction and the quantitative regression as a whole joint operation instead of linearly performing the operations one by one, and overcomes the defects of the step-by-step strategy in the prior art.

This invention proposes a drug target interaction prediction method based on graph interaction and multi-granularity fusion, belonging to the field of bioinformatics. Addressing issues such as oversmoothing of graph neural networks in heterogeneous graphs, insufficient utilization of drug-target interactions and multi-scale information, and poor adaptability of multi-branch feature splicing, an improved method is proposed. First, a deep interactive graph neural network branch is constructed on the node adaptive local smoothing features and the heterogeneous graph composed of drug-drug, target-target, and drug-target edges, realizing message passing and fusion of classification edges and introducing residual connections. Second, a dual-tower interactive branch is constructed for multi-order interactions. Third, a multi-granularity fusion branch is constructed to achieve progressive fusion of multi-scale features. Finally, the multi-branch representations are weighted and fused through a gating network, combined with the output results of a perceptron and a sigmoid function, and optimized using binary cross-entropy. This invention improves prediction performance and interpretability, and is applicable to scenarios such as drug discovery and target screening.

This invention discloses an active peptide from sturgeon cartilage that promotes height growth and improves bone development, along with its preparation method and applications, belonging to the field of active peptide technology. Using sturgeon cartilage as raw material, this invention selects the polypeptide with the highest activity in promoting bone growth and development from different enzymatic hydrolysis combinations through MC3T3-E1 cell experiments, and verifies its positive effect using zebrafish experiments. Subsequently, two potential active peptide sequences from sturgeon cartilage with bone growth and development promotion functions are screened using mass spectrometry to identify their sequences and bioinformatics techniques such as molecular docking. The amino acid sequences of the active peptide segments are shown in SEQ ID NO.2 and SEQ ID NO.5. The sturgeon cartilage peptides prepared by this invention have activity in promoting bone growth and development, which can solve the problem of high-value utilization of sturgeon and can also be used as a functional factor in functional products, showing good application prospects.

The present application belongs to the technical field of bioinformatics, and specifically relates to a chronic atrophic gastritis biomarker screening method and system based on a multi-modal differential network. The present application comprises: preprocessing metabolome data and microbiome data, respectively constructing intra-modal interaction graphs and inter-modal interaction graphs, and obtaining a multi-modal differential network by making a difference between the interaction graphs under different clinical groupings; based on the constructed differential network, a multi-level difference evaluation index is constructed: CDI is calculated at the node level to quantify the network topology and biological function differences of the biomarkers; at the structure level, a group specificity index GSI is defined to characterize the group specificity of the differential network structure; the feature ranking results obtained by CDI and GSI are fused and screened to obtain a biomarker set for atrophic gastritis classification prediction. The present application improves the effectiveness of biomarker screening under the condition of small sample and high dimension, and provides support for subsequent targeted cultivation of candidate colonies and metabolites and development of potential intervention target points.

The invention belongs to the technical field of bioinformatics and medical detection, and particularly relates to a marker combination for neuroblastoma (NB) noninvasive risk level grading, a prediction model, a prediction method and application thereof. The marker combination is used for determining the sex, determining whether the month age is greater than 18 months, determining whether plasma MYCN is amplified, determining whether tumors are metastatic, and determining the content of neuron-specific enolase and lactic dehydrogenase; a machine learning algorithm is used for constructing an NB noninvasive risk degree grading prediction model, the comprehensive performance of the random forest model is optimal, the area value under a subject working characteristic curve reaches 0.956, the sensitivity is 92.9%, the specificity is 82.1%, the accuracy rate is 87.5%, the Kappa value is 0.75, the F1 score is 0.881, and NB middle and low risk patients and NB high risk patients can be effectively distinguished; the NB non-invasive risk level grading prediction model constructed by the invention can quickly, accurately and non-invasively perform NB risk level grading, and has a relatively good clinical application value.