291 results about "Genomic data" patented technology

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 provides a laryngeal cancer multi-mode prognosis prediction method and a laryngeal cancer multi-mode prognosis prediction system fusing a CT (Computed Tomography) image and a ViT model. Relates to the technical field of biomedical images. The method comprises the following steps: acquiring and preprocessing multi-modal data of a laryngocarcinoma patient; carrying out lightweight compression, redundant information screening and robustness training on the ViT model to obtain an optimized ViT model; extracting depth features of the CT image data based on the optimized ViT model, and performing multi-stage fusion on the depth features and clinical and genome data to construct a prognosis prediction model; and performing risk stratification on the patient according to a prognosis prediction result predicted by the prognosis prediction model, and outputting treatment guidance suggestions based on the risk stratification. Through ViT model optimization, multi-modal data fusion and clinical adaptation design, precise prediction and personalized treatment guidance of laryngocarcinoma prognosis are realized, and the problems of insufficient image degradation processing, low model deployment efficiency and the like in existing laryngocarcinoma prognosis prediction are solved.

The invention relates to the technical field of genome data analysis, and discloses an intelligent system, method and equipment for assisting multi-step genome data analysis, and the system comprises a dialogue agent which is used for generating a corresponding answer according to a question of a user, or reading an analysis plan file generated by a workflow agent, generating an analysis interpretation text for the analysis plan file; the workflow agent is used for generating a structured task execution plan according to the to-be-executed analysis task and executing the to-be-executed analysis task; and the modeling analysis agent is used for generating a configuration file and a script based on the user request, constructing a model and generating an analysis result corresponding to the user request in combination with the workflow agent. Through multi-agent cooperation, task division and cooperative scheduling are realized, each agent independently completes task planning, execution control, model analysis and other functions, the bottleneck problem of processing of a traditional single model in a complex process is avoided, error accumulation is reduced, and the execution efficiency and stability of the whole process are improved.

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 medical image automatic identification system based on a neural network, and relates to the technical field of medical image identification. The method is used for solving the problem that early recognition of neurodegenerative diseases is difficult due to medical image and genome data splitting and poor model interpretability in the prior art. The method comprises the following steps: firstly, extracting multi-scale features of a brain structure through a three-dimensional convolutional neural network and a self-attention mechanism, calculating a multi-gene risk score based on a risk site, and encoding the score into a feature vector; secondly, using a cross attention mechanism to take gene features as query vectors, fusing the gene features with image features, and generating brain structure anomaly features under gene regulation; then, gradient weighting class activation mapping is applied to generate a visual thermodynamic diagram, and gene-image association weight weighting is combined to construct a brain region risk distribution diagram; and finally, a high-risk brain region space coordinate set is extracted through threshold segmentation, and an accurate quantification basis is provided for early recognition.

The invention discloses an identification and analysis method for unknown pathogenic microorganisms, which comprises the following steps: filtering out genome sequences with low integrity, pollution and tag errors, and establishing a high-quality virus identification database; constructing a virus host prediction model through a machine learning algorithm; unknown pathogenic microorganisms are identified and analyzed, potential hosts or pathogenicity of the unknown microorganisms are identified, and whether the unknown microorganisms are unknown pathogenic viruses or bacteria or not is further judged. On the basis of metagenome data analysis, potential unknown pathogenic microorganisms in samples of human bodies, environments and the like can be identified more accurately.

The invention discloses application of a gene marker in early screening of esophagus, stomach and intestine multiple cancer species, an early screening model construction method and a detection device, and belongs to the technical field of early noninvasive detection of digestive tract tumors. By analyzing the whole genome characteristics of circulating free DNA in peripheral blood, a novel multi-cancer-species screening system is established. On the basis of low-depth whole genome sequencing data, molecular markers in three dimensions, namely a genome copy number variation mode, a DNA fragment distribution characteristic with a specific length and an epigenetics signal of a transcription initiation region, are emphatically detected. An advanced converter neural network architecture is adopted, and the model can efficiently capture complex feature association in a whole genome range through a specific self-attention mechanism. The model design particularly considers the particularity of genome data, introduces an adaptive position coding system, and accurately reflects the spatial distribution relationship of DNA fragments on chromosomes. Therefore, the system can still maintain excellent detection performance under extremely low sequencing depth.

The invention provides an employee health risk monitoring method and system based on big data, and relates to the technical field of health management and data analysis, and the method comprises the steps: S1, collecting the multi-modal health data of an employee, including but not limited to physiological data, behavior data, working environment data, social interaction data and genome data, and S2, fusing and analyzing the collected multi-modal health data by adopting an integrated neural network model and an adaptive learning algorithm. According to the big data-based staff health risk monitoring method and system, through the combination of the integrated neural network and the deep causal inference model, the multi-modal health data of the staff can be deeply analyzed, and the complex causal relationship between the health data can be effectively identified. Through the dynamic analysis of the historical health data, the current health state and the working environment of the employees, the model can achieve the precise health risk prediction, and provides personalized health intervention suggestions according to the individual differences of the employees.

A system and methods for upsampling of decompressed genomic data after lossy compression using a neural network integrates AI-based techniques to enhance compression quality. It incorporates a novel deep-learning neural network that upsamples decompressed data to restore information lost during lossy compression, taking advantage of cross-correlations between genomic data sets.

A genomic data analyzer workflow may be configured to identify, with a variant annotation module, subsets of patient variants which match at least one medical reference variant database entry, even if the variant calling information in genomic data analyzer workflow and the database use different variant representations of SNP, MNP, INDELS and DELINS. In particular, database variants which are included into a subset of patient variants may be identified even if they do not exactly match the corresponding strings. The variant annotation module may be adapted to apply a branch-and-bound-like algorithm to efficiently process all possible subsets of patient variants in a genomic region.

A system and methods for upsampling of decompressed genomic data after lossy compression using a neural network integrates AI-based techniques to enhance compression quality. It incorporates a novel deep-learning neural network that upsamples decompressed data to restore information lost during lossy compression, taking advantage of cross-correlations between genomic data sets.

The invention belongs to the field of molecular genetics, and particularly relates to an SNP (Single Nucleotide Polymorphism) molecular marker combination for genetic relationship identification of wuzhu cattle and application of the SNP molecular marker combination. The SNP marker combination is composed of 140 high-polymorphism SNP sites distributed on a plurality of chromosomes of a bovine whole genome, the average distance between the sites is reasonable, and the SNP marker combination has good genome coverage. The selected SNP site has high allele frequency (MAF), expected heterozygosity (He) and polymorphic information content (PIC), and the cumulative exclusion probability is 0.9999. King and Plink software are combined to deduce a genetic relationship coefficient (Kinship) and an IBD shared value (PIHAT) between samples, and a result shows that the marker combination can realize a genetic relationship recognition effect equivalent to that of whole genome SNP data in Wuzhu white cattle (grassland white cattle), and the scientificity and practicability of the marker combination are verified. The SNP molecular marker combination can be widely applied to the processes of germplasm resource management, pedigree correction and cattle breeding, and has good popularization prospects and economic benefits.

The disclosed multiscale framework includes innovatively coupling genome-scale metabolic models with an agent-based model and an adapted continuum model of the biofilm; employing a systematic bottom-up approach to identify interrelationships between local substrate and mediator transport and the dynamic biofilm characteristics; and elucidating the interdependence of genomic data and microscale biofilm properties, thereby enabling a deeper understanding of the behavior of species within the biofilm.

The invention provides a method for evaluating a set of input data, the input data comprising at least one of: clinical data of a subject; genomic data of a subject; clinical data of a plurality of subjects; and genomic data of a plurality of subjects, using a deep learning algorithm. The method includes obtaining a set of input data, wherein the set of input data comprises raw data arranged into a plurality of data clusters and tuning the deep learning algorithm based on the plurality of data clusters. The deep learning algorithm comprises: an input layer; an output layer; and a plurality of hidden layers. The method further includes performing statistical clustering on the raw data using the deep learning algorithm, thereby generating statistical clusters and obtaining a marker from each statistical cluster. Finally, the set of input data is evaluated based on the markers to derive data of medical relevance in respect of the subject or subjects.

The invention provides a microbial ecological interaction mechanism determination method and device, and relates to the technical field of species correlation research. The method comprises the following steps: determining N microorganisms and abundance thereof based on metagenome data of a target environment sample; determining the abundance grade and correlation based on the abundance of the ith microorganism and the jth microorganism; carrying out metabolic function annotation on the i microorganism and the j microorganism based on a reference genome, and calculating a function overlapping ratio; determining a community construction mechanism based on the abundance correlation and the function overlapping ratio; based on the function overlapping difference, determining a function overlapping grade change; and determining the ecological interaction mechanism of the i and j microorganisms according to the information. The method can be used for systematically revealing a species coexistence mechanism and a metabolic interaction relationship thereof in the microbial community.

The invention provides a non-lineage animal pairing evaluation method based on genome data, and belongs to the technical field of bioinformatics and protection genetics, the method comprises the following steps: firstly, obtaining whole genome sequencing data of all individuals in a population, and carrying out quality control to obtain a high-quality SNP site set; aiming at all possible male and female pairing combinations in the population, calculating an inter-parent genetic coefficient depKin, an inter-parent heterozygous difference ratio HDR and an inter-parent potential risk load index GRLI, and predicting a fixed proportion Proh of ROH of offspring; performing z-score standardization processing on the four genetic indexes: performing principal component analysis on standardized data, extracting the first two principal components PC1 and PC2, determining a weight according to a variance contribution rate, and calculating a pairing comprehensive score; and generating a pairing candidate recommendation list for each individual according to the comprehensive score, and screening a high-quality pairing scheme. The method disclosed by the invention can be completely independent of pedigree records, and genetic evaluation is directly carried out based on genome data.

The invention relates to a computerized method for compressing genome sequencing data. The method comprises the following steps: comparing the genome sequencing data with reference sequencing data; obtaining one or more differential read sequences, each of the one or more differential read sequences being a read sequence of the genomic sequencing data that is different from a corresponding read sequence of the reference sequencing data; and obtaining compressed genomic sequencing data by compressing the one or more difference segment sequences using a statistical compression method or using an assembly method with a probabilistic data structure. In some embodiments, the method also has the step of assembling the plurality of reads to form reference data. In some embodiments, the method also has the step of storing the compressed genomic data in the blockchain.

The invention relates to the technical field of carbon emission processing, in particular to a visual building construction process carbon emission calculation method, which comprises the following steps of: endowing each building component with a component identifier, binding carbon footprint data of a building material corresponding to the building component in a construction stage, and correspondingly generating a plurality of target carbon units; combining the plurality of target carbon units to generate a carbon genome data model; according to a preset carbon emission factor, converting resource consumption data and construction activity data generated in the current building construction process into a carbon emission data stream; fusing the carbon emission data flow with the carbon genome data model, updating the carbon emission state of each target carbon unit in the building construction process, generating carbon emission distribution information mapped with the three-dimensional space position of the building and the construction progress of the building, and performing visual rendering on the carbon emission distribution information, the technical problem that significant deviation exists between the calculation result and the carbon emission actually generated in the construction process is solved.

This invention discloses a multi-scale prediction method and system for the maximum growth rate of soil microorganisms based on genomic features, involving the interdisciplinary fields of microbial ecology and genomics. The method includes: genomic data preparation, core genomic feature extraction, single-scale growth rate prediction, community-scale extrapolation, and result verification and output. The system includes a genomic data processing unit, a core feature extraction module, a growth rate prediction model, a community-scale extrapolation module, and a result visualization and verification unit. This invention overcomes the limitations of traditional culture methods and in-situ measurement methods, achieving high-throughput and accurate prediction of the growth rates of culturable and uncultured microorganisms, and solving the problems of low coverage, fragmented scales, and high costs of existing technologies. It is applicable to soil microbial function assessment, carbon cycle model parameterization, and ecosystem management.

The application provides a method for identifying chromosomal translocation by integrating three-dimensional genome and third-generation genome data, and relates to the technical field of biology, and the method comprises the following steps: obtaining third-generation genome sequencing data of a sample to be tested, identifying chromosomal translocation based on the third-generation genome sequencing data to form a first data set; obtaining Hi-C sequencing data or sequencing data based on a Hi-C derived technology of the sample to be tested, and identifying chromosomal translocation based on the Hi-C sequencing data or the sequencing data based on the Hi-C derived technology to form a second data set; taking candidate chromosomal translocation in the intersection of the first data set and the second data set, and taking verified candidate chromosomal translocation as the final screening chromosomal translocation. The chromosomal translocation screened by the method has high accuracy, and the accuracy can reach 98%.

The application belongs to the field of fish development, and particularly relates to a molecular marker for identifying Nibea albiflora sperm-induced Pseudosciaena heteroclita gynogenesis offspring and hybrid offspring, a primer set thereof, a kit and application. The primer set for identifying the molecular marker of the Nibea albiflora sperm-induced Pseudosciaena heteroclita gynogenesis offspring and hybrid offspring comprises primers F and R, the nucleotide sequence of the primer F is shown as SEQ ID NO. 1, and the nucleotide sequence of the primer R is shown as SEQ ID NO. 2. Based on genomic data analysis, the application develops the molecular marker F / R which can effectively identify the Pseudosciaena heteroclita gynogenesis offspring and hybrid offspring in the juvenile stage. The method comprises extracting genomic DNA of parents and offspring, PCR amplification and agarose gel electrophoresis detection, and finally distinguishing the gynogenesis individuals and hybrid individuals by whether the Nibea albiflora parent marker is contained in the electrophoresis band.

A system, method and apparatus include one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform operations for hardware-accelerated execution of a genomic data processing pipeline based on one or more user-selectable options presented via a graphical user interface. The operations include obtaining first data representing a selection of one or more of a plurality of user-selectable options submitted via the graphical user interface. One or more of the plurality of user-selectable options identify a particular genomic data processing pipeline. The operations further include configuring, using the application programming interface executed by the one or more computers, an integrated circuit to perform one or more hardware accelerated steps of a primary, secondary, and / or tertiary processing protocol of the particular genomic data processing pipeline.

A computer-implemented method (100) for representing a graph genome data structure in a memory of a computer system, the method comprising: receiving (120) a definition of a graph reference genome, comprising: (i) a plurality of graph genome edges, each specifying a sequence of characters, and (ii) a plurality of links representing connections between the plurality of graph genome edges; generating (130) a graph data structure for the received graph reference genome definition, comprising a reference sequence count specifying a number of a plurality of reference sequences; and storing (140) the generated graph data structure in memory.

The invention relates to a computer-implemented method for predicting a genome-related feature (458) from genomic data of multiple individuals (402), the method comprising: —receiving (102) genomic marker data (434, 442) of each of the individuals, the genomic marker data being indicative of a plurality of first marker positions assigned to identified marker variants (1140-1142) and multiple second (1144) marker positions have a missing or ambiguous marker variant assignment; —computing (104) a haplotype-block library (448) comprising a plurality of haplotype-blocks (1126-1136), each haplotype-block comprising start and stop coordinates and a series of marker positions referred to as ‘comparison marker positions’ lying within the start and stop coordinates; —performing (106) a haplotype-block-guided marker imputation; —supplementing (108) the genomic marker data with the imputed marker variants; and—using the supplemented genomic marker data (454) for computationally predicting the feature (458) of the individuals.

The invention belongs to the technical field of pollution control, and relates to a soil pollution treatment method and system utilizing microbial remediation, and the method comprises the following steps: obtaining target remediation functional microbial agent genome and to-be-remedied site native microbiome metagenome data, and constructing a biological information basic data set; processing the data, and constructing an interaction network model containing microbial inoculum and native species nodes based on metabolic complementarity and ecological niche overlapping degree simulation calculation; analyzing the network topology structure to screen a key native co-generation node set, and generating a growth promotion demand map; determining a targeted metabolism regulation factor with targeted selectivity based on atlas reverse matching, and generating a targeted signal instruction; executing the instruction and putting a microbial agent, activating a synergistic node in situ and coupling with the microbial agent to construct a degradation function network; the method solves the problem that the colonization efficiency of the exogenous functional microbial inoculum is not high due to lack of accurate regulation and control on the native microbial community.

A method for constructing a database for discrimination of microorganisms according to the present disclosure includes: a step (S12) for acquiring genome data of two kinds of microorganisms; a step (S14) for predicting a group of proteins produced by each of the two kinds of microorganisms; a step (S16) for producing a list of mass-charge ratios of each of the two kinds of microorganisms; a step (S20) for calculating the degree of similarity between the lists of the mass charge ratios; a step (S32) for generating information that includes the fact that the two kinds of microorganisms cannot be discriminated by MALDI-MS when the degree of similarity is equal to or larger than a predetermined value; and a step (S36) for outputting the information.

Provided herein are methods and systems for detecting polynucleotide sequences encoding viral capsids in metagenomic data. The methods and systems disclosed herein may include a sequence alignment-based module, a gene-based data processing module, and further characterization of putative viral sequences to identify novel polynucleotide sequences encoding viral capsids in metagenomic data.

The application discloses a gene editing system CRISPR-Cas12p and application thereof. Based on microbial genomes and metagenomic data, the CRISPR-Cas12p protein of the CRISPR-Cas protein family is obtained by preliminary screening by using a Prodigal gene prediction tool, a Pfam database and HMMER software, and a gene editing system CRISPR-Cas12p is constructed. PAM preference and interference function identification show that the editing system has a PAM preference of 5'-TTC-3', can effectively realize targeted cutting by using long transcripts and double RNA hybrid chain transcripts respectively, and can realize editing of a target gene in prokaryotic and eukaryotic cells. The CRISPR-Cas12p gene editing system obtained by the application has a small protein component, is beneficial to delivery, can realize gene editing in prokaryotic and eukaryotic cells, and has a wide application prospect.

Genomic data is written to disk in a compact format by dividing the data into segments and encoding each segment with the smallest number of bits per character necessary for whatever alphabet of characters appears in that segment. A computer system dynamically chooses the segment boundaries for maximum space savings. A first one of the segments may use a different number of bits per character than a second one of the segments. In one embodiment, dividing the data into segments comprises scanning the data and keeping track of a number of unique characters, noting positions in the sequence where the number increases to a power of two, calculating a compression that would be obtained by dividing the genomic data into one of the plurality of segments at ones of the noted positions, and dividing the genomic data into the plurality of segments at the positions that yield the best compression.

The invention discloses an antibiotic resistance prediction method based on a metric element learning architecture. According to the framework, an adaptive mechanism is adopted, and the characteristics of different antibiotics and the distribution characteristics of genome data are precisely matched with an adaptive machine learning model by deeply analyzing the characteristics of the different antibiotics and the distribution characteristics of the genome data. Escherichia coli whole genome sequencing data is adopted, and prediction research is carried out aiming at whether antibiotics have drug resistance or not, so that an optimal prediction model under different drug types and data distribution conditions is evaluated. In addition, molecular structure characteristics and data distribution modes of antibiotics are deeply excavated, and a metrics-based meta learning model matching mechanism is constructed. According to the method, through a metrics-based meta learning framework, the problem of model selection in antibiotic resistance prediction in a complex scene can be effectively solved; meanwhile, in a new drug resistance prediction task, a zero sample learning ability is realized, model adaptation can be completed without extra training data, and the computing resource consumption cost in a model training process is effectively reduced.