361 results about "Sequencing data" patented technology

This invention provides a data storage method and system for tuberculosis detection, relating to the field of tuberculosis detection technology. The data storage method for tuberculosis detection includes the following steps: S1. Collecting whole-genome sequencing data of Mycobacterium tuberculosis, host serum IgG titer, and drug sensitivity test results; S2. Calculating genetic distance D based on a reverse evolution model to generate four-dimensional spatiotemporal coordinates (t, x, y); S3. Performing data partitioning and storage based on the drug target barrier value β; S4. Generating dynamic metadata using a host-pathogen dynamics model and compressing and storing it. This invention implements a dynamic storage entropy adjustment algorithm at the hardware and software collaborative level, continuously optimizing the matching efficiency of data compression and physical storage. This results in an intelligent data hub that can perceive the evolutionary pulse of pathogens and autonomously optimize resources, providing support for clinical tuberculosis prevention and control decisions with temporal depth, spatial correlation, and risk evolution.

This invention discloses an efficient RNA data preprocessing method to address the problems of low processing efficiency and low information density in high-throughput sequencing data. Its core steps include: (1) introducing a parallel processing scheme for high-throughput sequence data, rapidly mapping RNA-seq data to a reference genome to generate a BAM file; (2) extracting base sequences and expression levels and storing them as compact PKL format files; (3) extracting all exon position information by parsing the genome annotation file; (4) combining multi-sample expression level data to screen for highly variable exons and constructing a high-information-density feature list based on the sample set; and (5) accurately extracting target sequences from the preprocessed file based on this list. Compared to traditional methods, this innovative approach achieves triple optimization: full-process parallel processing for accelerated computation, high-compression data storage, and adaptive feature selection. Processing speed is increased by 3-5 times, and data volume is reduced by more than 90%, making it suitable for high-throughput RNA-seq data analysis with large sample sizes.

The present application relates to the technical field of knowledge graph, in particular to a multi-task hepatitis B drug screening method and system based on knowledge graph assistance, comprising the following steps: based on hepatitis B virus genotype sequence data recorded over time, patient drug use records and drug sensitivity. In the present application, multi-dimensional dynamic time series data such as virus genotype sequence data, patient drug use records and drug sensitivity are integrated, the interaction events between entities are marked by time stamp, the dynamic characteristics such as virus variation track and drug efficacy change are embedded in the graph node attributes, so that the knowledge representation can reflect the time dependence in the real scene. Based on biological pathway annotation information and protein interaction data, the hyperedge connection multi-entity set is defined, the limitation of traditional knowledge graph which only supports binary relationship is expanded, the drug combination and multi-target synergistic mechanism are explicitly modeled, and the misjudgment of combination effect caused by the simplification of interaction relationship is avoided.

This disclosure relates to a technique for identifying tumor-specific mutations through integrated analysis of next-generation sequencing data using machine learning models. In certain embodiments, a computer implementation method is provided, comprising: generating sequence reads from one or more samples collected from the same patient; generating variant calling files by analyzing the sequence reads corresponding to each of the one or more samples; generating a list of candidate somatic variants by comparing the variant calling files; generating a score for each of the candidate somatic variants in the list of candidate somatic variants using a classification machine learning model, wherein the score is generated based on a plurality of classifications generated by the classification machine learning model; determining the ctDNA status for the patient based on the score, wherein the ctDNA status is either positive or negative; and generating a report providing the ctDNA status for the patient.

This application provides a runoff prediction method, apparatus, electronic device, and computer-readable storage medium. The method includes: acquiring the forecast meteorological time series of a target watershed during the prediction period, historical meteorological time series, and historical runoff time series for historical periods; inputting the historical meteorological time series and historical runoff time series into an attention model to extract global contextual features of the target watershed; inputting the forecast meteorological time series, global contextual features, and initial noise data into a conditional diffusion probability model, performing multiple backdiffusion processes to obtain multiple predicted runoff time series of the target watershed during the prediction period; and calculating a specified quantile for each moment in the prediction period based on the multiple predicted runoff time series to construct a confidence interval, thereby obtaining runoff prediction information containing a risk probability distribution. This method avoids gradient vanishing when processing long-sequence data and outputs the probability distribution of the prediction results.

The application provides a system state updating method, a system and a storage medium. The method comprises the following steps: acquiring environment data collected by a plurality of sensors in a preset period, wherein the environment data at least comprises a measurement value and a corresponding time value; performing state prediction based on the plurality of measurement values to obtain a predicted system state value; and updating a current system state based on the predicted system state value. The method provided by the application effectively solves the technical problems that the classic filtering algorithm cannot process out-of-sequence data, and the data loss, performance decline and large state prediction error caused by the influence of abnormal measurement values of a single sensor. The data integrity is effectively improved, and the state prediction accuracy is improved.

This invention discloses a product carbon footprint data management system and method based on the Internet of Things (IoT), relating to the field of environmental protection technology. The method includes: collecting raw data from each stage of a product's entire lifecycle to generate carbon genome metadata; calculating the stage-specific carbon emissions of the product at each stage of its lifecycle based on the carbon genome metadata; constructing a carbon genome sequence of the product based on the carbon genome metadata and the corresponding stage-specific carbon emissions, and performing time-series pattern analysis based on the carbon genome sequence; summarizing the stage-specific carbon emissions of each stage of the lifecycle to calculate the product's total lifecycle carbon footprint; generating a standardized carbon footprint index based on the product's total lifecycle carbon footprint and a dynamic benchmark value; calculating the product's carbon footprint for future periods based on historical carbon genome sequence data; and generating carbon reduction operation parameters based on the product's future carbon footprint prediction results. By accurately collecting and correcting data through IoT sensors, combined with carbon footprint prediction, scientific carbon emission management is provided.

This invention relates to a method for structural variant (SV) identification and genotyping in long-read family pedigree samples. The invention pertains to the field of vegetative variant (SV) detection in families, specifically focusing on methods for identifying and genotyping structural variants. The aim of this invention is to address the problems of existing family-based SV detection methods, which heavily rely on high-coverage sequencing, resulting in insufficient utilization of genetic characteristics and inaccurate SV detection and genotyping, as well as the high cost of sequencing multiple samples. This invention uses individual sequencing data from all family members as input, extracts variant features from each member, performs cluster analysis on the family feature set, assigns features to their respective members, and then uses three family feature signal correction methods to correct detection errors. Finally, SVs are located and anchored using Mendelian inheritance laws, and haplotype genotyping of SVs is completed using linkage information from long-read sequencing fragments.

The application discloses a regional tourism industry operation monitoring and decision support method and system, and belongs to the technical field of tourism industry operation management, and comprises the following steps: obtaining convergent concurrent load time sequence data of each regional monitoring node; obtaining physical passenger flow characteristic vectors of each regional monitoring node; generating a space structure characteristic matrix; detecting data deviation anomalies of the convergent concurrent load time sequence data and dynamically adjusting fusion weights, synthesizing comprehensive state characteristic vectors of each regional monitoring node; constructing a state conduction graph model and outputting prediction result data for triggering regional resource scheduling; and through the establishment of a base station-node mapping relationship and the introduction of a spatial overlap area weight distribution mechanism, the application realizes the accurate attribution of load data in a physical space. In combination with characteristic vectors constructed in dimensions such as spatial distribution entropy, the application can comprehensively depict the regional state from four dimensions of total amount, stability, dispersion and abnormal impact, and greatly reduces the system false alarm rate.

A method and device for evaluating the influence effect of an academic recommendation algorithm based on a social robot, the method comprising: collecting academic papers, citation relationships and author information through a data interface or a web crawler, and screening target personnel data according to a research field; constructing a virtual social robot and establishing a research interest vector of the virtual social robot based on historical literature and research theme information of the target personnel; presetting multiple literature acquisition strategies for the social robot to form differentiated experimental conditions; controlling the social robot to perform operations such as paper retrieval, access and click recommendation on an academic platform, and recording literature access paths, recommendation results and browsing sequence data in real time; calculating the research theme distribution of the social robot according to a literature set contacted in the experiment, and comparing the research theme distribution with the initial research interest, so as to obtain the change degree of the research direction; and comparing the change results of the research direction of the social robot under different strategies, and quantitatively evaluating the influence degree of the recommendation system on the research direction evolution of the scientific researchers.

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.

In some embodiments, a computer-implemented method of detecting a presence of a predetermined fusion gene in a biological sample is provided. A computing system generates an alignment of a read sequence to a reference genome. The alignment includes a first alignment result and a second alignment result. The computing system determines a breakpoint location indicated by the first alignment result and the second alignment result, distances between coordinates of the breakpoint location and coordinates of one or more expected breakpoint locations associated with the predetermined fusion gene, a gap size value and an overlap size value. In response to determining that the gap size value is less than a gap size value threshold, the overlap size value is less than an overlap size value threshold, and the distances are less than a breakpoint distance threshold, the computing system generates an indication of the presence of the predetermined fusion gene.

The application relates to a mycobacterium tuberculosis drug resistance phenotype prediction method and device and a computer device. The method comprises the following steps: determining a to-be-predicted mutation site which does not exist in a mutation phenotype annotation database from at least one mutation site of a target gene fragment of to-be-processed mycobacterium tuberculosis according to gene sequencing data of the mycobacterium tuberculosis; converting protein structure change data of the to-be-predicted mutation site according to protein structure data corresponding to the to-be-predicted mutation site; inputting the protein structure change data of the to-be-predicted mutation site into a drug resistance phenotype prediction model to obtain a predicted drug resistance phenotype of the to-be-predicted mutation site; and determining a drug resistance phenotype prediction result of the to-be-processed mycobacterium tuberculosis based on the predicted drug resistance phenotype of the to-be-predicted mutation site and drug resistance phenotypes of the mutation sites except the to-be-predicted mutation site. The method can effectively improve the mycobacterium tuberculosis drug resistance phenotype prediction efficiency.

This invention provides a method and apparatus for splitting single-cell mixed sample sequencing data, relating to the field of biotechnology. The splitting method includes: capturing and sequencing single-cell suspensions using a single-cell platform; then performing reference genome alignment, cell identification, and gene expression level quantification on the sequencing data using Cellranger; splitting the cell data identified in step a into two groups of cell data for different sexes based on SNP locus information from the 1000 Genomes Project; and distinguishing the two groups of cell data from male or female samples based on the proportion of sex-specific genes expressed in the two groups of cell data. This splitting method eliminates the need for additional experimental operations such as protein labeling and genome sequencing, and can provide accurate and reliable data splitting even when individual SNP information is unavailable.

This invention relates to a method for continuous motion prediction in patients with cerebral palsy. The method includes: acquiring sEMG data, IMU data, and ground truth joint angles of the subjects and constructing a dataset; dividing the dataset into training, validation, and test sets for each subject according to time sequence; standardizing the collected EMG and IMU data for each subject to obtain standardized sequence data; inputting the standardized sequence data of each subject into a trained prediction network, which includes parallel TCN and GAT modules. The TCN module extracts IMU temporal features, and the GAT module extracts sEMG spatial features. The two types of features are fused through a multimodal fusion module and then input into a BiLSTM network for bidirectional temporal modeling to capture the bidirectional temporal dependence of movements. Finally, a regression output module outputs the standardized predicted angles. Compared with existing technologies, this invention has advantages such as achieving continuous and accurate motion prediction for patients with cerebral palsy and enhancing cross-subject generalization ability.

This invention discloses a method, device, and program product for index sequence orientation identification in gene sequencing. The method includes: acquiring a designed index sequence and sequencing index sequence data; obtaining a reference index sequence based on the sequencing index sequence data; performing a transformation operation based on the designed index sequence to obtain a transformed index sequence; and matching the transformed index sequence under various transformation modes with the reference index sequence to determine the index orientation of the designed index sequence.

Various methods and processes, apparatuses or systems, and media for classifying human behaviors in various domains by using ensemble learning to perform sequence modeling with respect to sequence data are disclosed. The method includes: receiving a first set of data; partitioning the first set of data into a set of respective data streams, each respective data stream corresponding to a respective agent; extracting, from a first data stream, a first sequence of observations that relates to a first agent; inputting the first sequence of observations to each of several models that are trained by using historical data relating to the first agent; using the models to generate a composite score that relates to the first sequence of observations; and determining, based on the composite score, whether the first sequence of observations indicates at least one anomaly that relates to a behavior of the first agent.

The application belongs to the cross field of virology and bioinformatics, and particularly relates to a virus identification model construction method and system based on capsid proteins. The method collects capsid protein sequence data and non-capsid protein sequence data, and trains a capsid protein identification model; obtains capsid protein sequences with known structures, structure information and hierarchical classification information, and identifies a few-sample category with a sample quantity lower than a preset threshold; generates supplementary data of the few-sample category through a protein sequence design model and screens the supplementary data from the collected capsid protein sequence data, predicts the three-dimensional structure of the sequence in the supplementary data, and together with the corresponding classification information, forms a capsid protein supplementary data set for training a capsid protein classification model. The application alleviates the problems of data imbalance caused by insufficient data of the few-sample category and high classification difficulty of remote homologous proteins caused by single feature extraction, and is helpful to realize efficient preliminary identification of viruses.

This invention discloses a ligand-binding protein, its preparation method, and its applications, relating to the field of biotechnology. Specifically, this invention discloses a ligand-binding protein of any one of a1) to a3): a1) a protein with the amino acid sequence shown in SEQ ID NO:1; a2) a protein with more than 90% identity and the same activity as the protein described in a1) obtained by substituting and / or deleting and / or adding amino acid residues to the sequence shown in SEQ ID NO:1; a3) a fusion protein obtained by attaching a tag to the N-terminus and / or C-terminus of a1) or a2). It can efficiently and specifically suppress gDNA interference, reduce gDNA background noise during NGS library construction, and improve the purity of the library and the reliability of subsequent sequencing data.

The application belongs to the field of medical prediction, and discloses an information mining method for heterogeneous time series data, comprising: acquiring electronic medical record data and constructing a hypergraph, analyzing and calculating the hypergraph to obtain embedding representation data, weighting the embedding representation data based on an attention mechanism to obtain embedding sequence data, constructing a sequence learning model and performing hidden state access to obtain hidden representation data and weight data thereof, weighting the embedding sequence data to obtain embedding sequence hidden data; training the sequence learning model through time training parameter data, weighting the embedding sequence hidden data through the trained sequence learning model to obtain time dimension hidden data, constructing a full connection network to analyze the time dimension hidden data to obtain medical event prediction data. The technical scheme disclosed by the application can learn complex information in the time dimension by using time step information, and can obtain accurate medical event prediction results.

The application discloses a method and system for predicting protein drug binding sites based on a multi-modal dynamic graph, comprising: obtaining amino acid sequence data and three-dimensional structure data of a protein, and generating evolutionary conservation features, structure graph topology features and sequence features respectively; inputting the features into a multi-modal fusion encoder to generate first fusion features; inputting the first fusion features into a prediction decoder to generate initial prediction probabilities; iteratively updating the structure graph topology features according to a preset three-graph update rule according to the initial prediction probabilities and residue dynamic communication scores; re-inputting the updated structure graph topology features into the multi-modal fusion encoder and the prediction decoder, and repeatedly executing until a preset iteration number is reached, to generate final prediction probabilities; and generating a residue importance heat map and outputting a prediction report based on the final prediction probabilities through a gradient weighted class activation mapping algorithm. The application realizes the cooperative optimization of prediction and graph structure, and significantly improves the accuracy of binding site prediction.

This invention provides a method and system for analyzing database performance indicators based on a large model, relating to the field of data processing technology. The method includes: acquiring performance indicator data of the database during operation, and performing time-series generation and noise removal on the performance indicator data to form standardized indicator sequence data; vectorizing the indicator sequence data and calculating the correlation strength between each performance indicator; performing semantic modeling processing through a large model to extract nonlinear coupling features between multi-dimensional performance indicators, forming bottleneck identification data containing coupling relationship levels; constructing a resource conflict resolution mechanism to determine the scheduling priority between computing resources and storage resources, generating scheduling instruction data containing resource adjustment magnitude and execution order; and updating the multi-dimensional feature representation data based on the performance feedback results after database execution. This invention improves the accuracy of database performance indicator analysis.

The present application belongs to the field of biotechnology and genomics, and particularly relates to a method and system for determining the chromosome base number of Macrobrachium rosenbergii based on multi-omics joint analysis. The method obtains de novo assembly sequencing data and Hi-C sequencing data, generates a chromosome-level candidate assembly without presetting the number of chromosomes by using Hi-C interaction signals after primary assembly, and performs whole-genome collinearity alignment with no less than two published reference genomes; in combination with quality constraints such as collinearity continuity, Hi-C boundary characteristics and BUSCO / LAI, the candidate chromosome boundary is comprehensively judged and iteratively converged, and finally the chromosome base number and reviewable evidence chain are output. The embodiments show that the present application can identify and correct the number redundancy caused by over-splitting of the reference genome, determine the base number of Macrobrachium rosenbergii as n=57 (2n=114), and improve the objectivity and reliability of base number determination.

This invention relates to the field of intelligent manufacturing and industrial automation technology, specifically to an intelligent order scheduling and material management system for a milling and turning workshop. It includes a task parsing module, a feature mapping module, a state acquisition module, a potential field construction module, a scheduling calculation module, and a feedback execution module. The system receives task drawing data and bill of materials data, extracts geometric feature bounding dimension data and machining tool vector sequence data, and tensors and encodes them. It collects the current tool library configuration status parameters of the target machining equipment and the physical attribute parameters of work-in-process, constructs a continuous potential energy field distribution matrix, and dynamically refreshes it. It calculates the potential energy gradient descent direction of the multidimensional machining feature tensor flow, generates a task queue of associated paths and a theoretically estimated execution cycle time sequence, issues CNC machining task plans and automated guided vehicle (AGV) allocation instructions, and feeds back the actual cycle deviation correction value for closed-loop updates. This invention makes the scheduling results closer to the actual executable state of the workshop.

This invention discloses a methanogenic lyase homologous to PeiR lyase and its applications. The PeiR lyase protein, targeting the peptide bonds of the methanogenic cell wall, participates in the biological process of hydrolyzing the archaea cell wall, effectively killing methanogens and reducing methane production. Proteins homologous to this lyase possess potential methane-reducing potential. This invention utilizes a series of bioinformatics software to identify protein sequences encoded by methanogenic viruses from a large amount of rumen microbial virome sequencing data, and analyzes the structural homology between these proteins and PeiR. Ultimately, this invention successfully obtained a series of methanogenic lyases homologous to PeiR. To achieve the expression of these lyases, this invention designed multiple primers to synthesize the target sequences and successfully expressed them in a prokaryotic expression system. In vitro gas production experiments with the crude enzyme solution showed that these lyases are effective in reducing methane production.

The application discloses an unmanned aerial vehicle inspection task execution method and device and electronic equipment. The method comprises the following steps: receiving and responding to a predetermined inspection task, acquiring multi-modal sensor data; performing cross-modal semantic alignment and feature fusion operation according to the multi-modal sensor data to obtain a fusion feature vector; determining component instance information according to the fusion feature vector; generating a first flight control instruction of the unmanned aerial vehicle according to the component instance information, and controlling the unmanned aerial vehicle to fly according to the first flight control instruction; and constructing a line space topology graph according to visual imaging data, spatial depth point cloud data, attitude inertia sequence data, position coordinate data and component instance information. The application solves the technical problems of low tracking flight accuracy of the unmanned aerial vehicle, lagging update of line topology information and missing connection relationship in the related art.