69 results about "Gene recognition" patented technology

The invention provides a typical culture gene identification method for historical blocks in southern Fujian, which comprises the following steps of: dividing culture gene identification elements into a macroscopic class, a mesoscopic class and a microscopic class according to an identification principle; carrying out data collection, data arrangement, picture labeling and data division; the construction model comprises four sub-models, namely a building feature target detection model, a street aspect ratio judgment model, a color analysis model and a plane layout judgment model, and the four sub-models act together to form a fusion model to identify microscopic features, mesoscopic features and macroscopic features in the picture; and carrying out model training, model evaluation and model optimization on the fusion model by utilizing the training set and the test set obtained by constructing the database, exporting the trained model into a deployable format, and deploying the trained model on computer equipment to carry out typical culture gene identification on the southern Fujian historical block. According to the method, the accuracy and efficiency of typical culture gene identification of the southern Fujian historical block are improved, and culture genes contained in the picture can be identified and classified by inputting the picture.

The invention discloses a gene identification method based on a mask graph auto-encoder. The method comprises the following steps: 1, acquiring gene interaction data and omics characteristic data and preprocessing the data; 2, masking the processed data, specifically comprising two branches, a node masking module and an edge masking module; 3, inputting the masked network into a graph automatic encoder for training, wherein the graph automatic encoder learns embedded representation of the network by reconstructing nodes and edges of the network; and 4, obtaining low-dimensional embedding of the features through the trained encoder, and finally performing gene classification by using a logic regression classifier. According to the method, the node information and the structure information of the graph are concerned at the same time by respectively masking the nodes and the edges in the network, and the dependence of the feature training model on the label information is reduced in a self-supervised learning mode, so that the genes can be accurately classified.

The invention is suitable for the field of unmanned aerial vehicle management and control, and provides an unmanned aerial vehicle identity recognition countering method and system based on radio frequency hardware gene recognition, and the method comprises the steps: collecting a radio frequency signal transmitted by an unmanned aerial vehicle through employing a distributed heterogeneous radio frequency sensor array, and dynamically adjusting the sampling frequency according to the signal intensity, performing multi-scale time-frequency analysis on the collected radio-frequency signals, extracting multi-dimensional feature parameters from a time-frequency graph, constructing feature vectors, inputting the feature vectors into a recognition model fused by a deep convolutional neural network and a capsule network, recognizing the identity of the unmanned aerial vehicle, and if the unmanned aerial vehicle is an illegal unmanned aerial vehicle based on the identity of the unmanned aerial vehicle, judging whether the unmanned aerial vehicle is illegal or not; generating and sending a targeted countering strategy; according to the method and the system, a distributed heterogeneous sensor array and a self-adaptive sampling technology are adopted, so that the acquisition capability of unmanned aerial vehicle signals in different environments is effectively enhanced, radio frequency hardware gene features of the unmanned aerial vehicle can be accurately extracted and identified, and meanwhile, dynamic adjustment can be performed according to the state and the environment of the unmanned aerial vehicle.

The invention relates to a specific driver gene and common driver gene identification method based on federal transfer learning and deep learning algorithms, and the method comprises the steps: constructing a data set based on data of different cancer types; based on a multi-head attention mechanism, preprocessing the data set; based on the preprocessed data set, training a neural network model to obtain a gene recognition model; wherein the neural network model is constructed based on a Chebyshev graph convolutional network and a graph convolutional network, model training adopts a federated transfer learning method, and the training process is divided into a server side and a client side; based on the gene recognition model, cancer specificity and common driver genes across a tumor are identified. Compared with the existing method, the method provided by the invention can more accurately and efficiently identify the specific and common driver genes across tumors.

The invention belongs to the technical field of gene identification, and discloses a method for integrating multi-omics data to enhance genome prediction and candidate gene identification, candidate gene identification is verified through multi-layer evidence integration, and the verification comprises priority ordering based on gene contribution scores, function enrichment analysis, generic genome network verification and CRISPR / Cas9 experimental verification. Evaluation on a corn population (n = 174) containing complete genomics, transcriptomics, translational omics and proteomics maps shows that the framework is remarkably improved in grain character prediction and is improved by 2.9-12.3% compared with a genome selection baseline, and meanwhile candidate genes verified by experiments are recognized. The invention further verifies the universality of the framework to five traits on an arabidopsis thaliana population, and provides an open source software platform to promote the practical application of the framework in a breeding plan.

The invention relates to a pathogenic gene identification method and system based on multi-agent debate and a medium. The method comprises the following steps: acquiring gene detection data and clinical phenotype data; the data agent processes the acquired data, and calls a gene variation pathogenicity analysis operator and a molecular genetic large model to obtain first pathogenic gene information; the knowledge agent calls a molecular genetic large model to obtain second pathogenic gene information based on the first pathogenic gene information; the knowledge and data agents sequentially speak and debate pathogenicity of candidate pathogenic genes in the first or second pathogenic gene information on the basis of sorting results in the pathogenic gene information output by the knowledge and data agents; after each round of debate is finished, the debate agent judges whether the sorting results of the data and the knowledge agent on the candidate pathogenic genes are consistent or not, if the sorting results are consistent or the number of debate rounds reaches a threshold value, debate is finished, the debate agent conducts reasoning and outputs a result, and if not, the next round of debate is started. Compared with the prior art, the method has the advantages of no dependence on large-scale training data, high interpretability and the like.

The invention belongs to the technical field of biological feature recognition, and relates to a rice blast pathogenic gene recognition method suitable for small sample high-dimension data, the method comprises the following steps: processing collected data to obtain the expression quantity of a sample corresponding to each gene, then carrying out difference analysis to recognize a difference expression gene, and determining the expression quantity of the sample corresponding to each gene; constructing a PPI network according to the differentially expressed gene, and determining a key gene related to rice blast; and finally, constructing a CGAN-SAE deep learning model, and training a CGAN module by using the determined key gene, so that the CGAN module can generate simulated gene sample data which is difficult to distinguish from real data. And training the SAE module by using the simulated gene sample and the real sample, so that the SAE module can capture global features, and finally, further processing the extracted features through PCA to identify the core gene in the rice blast infection process. The method solves the problem of gene screening in a rice data set with a small number of samples and high dimensionality.

The application provides a personalized cancer driver gene identification method based on network propagation, and the implementation steps are as follows: obtaining related data of cancer driver gene identification; performing data fusion on abnormal genomics data; constructing a cancer gene regulation network of a group; constructing a personalized cancer gene regulation network; calculating influence score of a node pair; and obtaining a personalized cancer driver gene identification result through a network propagation process. In the process of constructing the personalized cancer gene regulation network, statistical analysis is performed on the interaction between each pair of genes of each patient, so that the deviation of the network model caused by abnormal expression of a single gene in the prior art is avoided; and in the process of network propagation, various genomics data and topological information in the network are integrated, the information abundance in the network model is improved, and the accuracy of identifying personalized driver genes is effectively improved.

The invention discloses a street space gene recognition and extraction method based on ancient customs drawings, which comprises the following steps: collecting the ancient customs drawings related to a to-be-studied regional range, and screening and classifying the ancient customs drawings; dividing a characteristic space scene in the ancient custom picture in combination with the space scene conversion marker; performing extraction on the spatial features of the street by combining the divided spatial scenes, and performing clustering on each street space; extracting space elements of each street space in the street space image and converting the space elements into text labels; based on the street space in the scene, analyzing to obtain the configuration relationship of the space elements, and combining with the function interaction relationship in the ancient customs drawing to form a combination relationship and performing screening; verifying a spatial element configuration relationship in the screened combination relationship to obtain a final street space gene; according to the method, the suitability of the path flow of space gene recognition extraction and the technical method is effectively improved.

The invention belongs to the technical field of intelligent prediction, and relates to a key gene identification method and device, computer equipment and a storage medium, and the method comprises the steps: obtaining original sample data; preprocessing the original sample data to obtain preprocessed sample data; performing word segmentation operation on the preprocessed sample data according to the HFkme r to obtain word segmentation data; performing vector conversion operation on the word segmentation data to obtain word segmentation vector data; constructing an initial BGAC model, and performing model training operation on the initial BGAC model according to the word segmentation vector data to obtain a target BGAC model; obtaining a to-be-recognized genome protein sequence; and inputting the genome protein sequence into the target BGAC model for key gene recognition operation to obtain a key gene recognition result. The method not only solves the problems of high time and high cost in key gene identification in traditional molecular biology, but also provides a precious microscopic view angle for deeply exploring the origin and evolution of insect wings, and the complex biological macro-evolution process is better understood.

The invention provides a spatial transcriptome tissue specific expression gene identification method and system based on a multi-dimensional statistical index, and belongs to the technical field of gene identification, the method comprises the following steps: obtaining gene expression data corresponding to a plurality of genes in different cells, different cells belonging to different types of tissues; the gene expression data is data processed based on a space transcriptome; for each gene, determining a basic statistical feature and a differential expression feature corresponding to the gene based on the gene expression data corresponding to the gene in different cells; determining specific index characteristics of the gene based on the basic statistical characteristics of the gene; and determining a tissue specific expression gene from the plurality of genes based on the basic statistical characteristics, the differential expression characteristics and the specific index characteristics corresponding to each gene. According to the invention, the accuracy of tissue specific expression gene recognition can be improved.

To provide a gene identification method capable of identifying a kind of each gene, even though multiple kinds of genes are present in a same zone.SOLUTION: A gene identification method comprises: a step for combining a melting temperature curve related to multiple kinds of genes, for forming a composite melting temperature curve; a step for using the composite melting temperature curve, for learning a gene identification model 123d set so as to output candidates of multiple kinds of genes in a minute region, at values including an intermediate value; a step for amplifying by PCR (polymerase chain reaction) an identification target gene in a solution distributed in multiple minute regions, then, from a digital PCR measurement unit for measuring fluorescent intensity of the solution distributed in the multiple minute regions, acquiring measurement data indicating the melting temperature curve for each of the multiple minute regions; and a step for inputting the acquired measurement data to the gene identification model 123d, for identifying the multiple kinds of genes in the minute regions.SELECTED DRAWING: Figure 6

The invention belongs to the technical field of microbiological detection, and particularly relates to a rapid detection method for listeria monocytogenes RAA-CRISPR / Cas12a and application of the rapid detection method for the listeria monocytogenes RAA-CRISPR / Cas12a. According to the present invention, based on the combination of the recombinase polymerase amplification (RAA) and the CRISPR-Cas12a system, the ultra-sensitive and rapid detection of the Listeria monocytogenes is achieved through the constant temperature nucleic acid amplification and the specific gene recognition; the sensitivity reaches 3.6 CFU / mL (bacterial liquid) and 3.78 copies / L (genome DNA); the detection time is shortened to 26 minutes, and the method can be applied to rapid detection of various environmental samples.

The invention discloses an animal breeding screening method and system based on digital traditional Chinese medicine pulse diagnosis, and relates to the technical field of animal breeding and breeding (breeding) screening, and the method comprises the following steps: building an animal physical sign digital gene bank, building a physical genetic digital gene identification model through an animal physical sign data large model, and establishing a physical genetic digital gene identification model; according to the method, female animals and male animals with corresponding excellent genetic genes are effectively identified according to breeding cultivation indexes, so that through continuous monitoring and re-evaluation, on one hand, a stable genetic gene variety can be formed, and on the other hand, female animals and male animals which temporarily have health problem markers due to short-term physiological reaction can be effectively identified; by means of the method, misjudgment caused by physiological reactions and other short-term signs is greatly reduced, it is guaranteed that female animals and male animals which have excellent physical genetic genes but are affected by temporary conditions are not mistakenly eliminated, and meanwhile it is guaranteed that female animals and male animals which really have physical genes and have defects can be accurately recognized and screened out.

The application discloses a landscape gene identification method based on machine learning, relates to the technical field of machine learning and data science, and comprises the following steps: constructing a multi-level landscape gene classification architecture, pre-training a machine learning model based on the multi-level landscape gene classification architecture, and obtaining a landscape feature identification model; then, acquiring landscape element data corresponding to a to-be-identified landscape, calling the landscape feature identification model to analyze the landscape element data, and obtaining a landscape index identification result; then, taking the landscape index identification result as the basis, acquiring a first landscape gene identification result by using a feature deconstruction method, acquiring a second landscape gene identification result by using a prototype-variation theory, and acquiring a third landscape gene identification result by using a digital analysis strategy; and finally, taking the three results as a target landscape gene identification result, so that the efficiency and accuracy of landscape gene text identification and extraction are improved, and the application of the landscape gene is facilitated.

The invention discloses a cancer driver gene identification method based on conflict perception Markov blanket discovery, which comprises the following steps: S1, carrying out a preliminary condition independence test, screening genes related to cancer phenotypes, and obtaining an initial candidate father-child node set ICPCT; s2, detecting independent or dependent behaviors of genes in the initial candidate father-child node set ICPCT under different condition sets, and screening to obtain a candidate father-child node set CPCT and a candidate mating node set CSPT; s3, performing multi-condition pruning operation on genes in the candidate partner node set CSPT, and obtaining a partner set SPT after screening; s4, performing father-child node filtering operation based on conflict perception on genes in the candidate father-child node set CPCT, and obtaining a father-child set PCT after screening; and S5, integrating the mating set SPT and the father-child set PCT to obtain a Markov blanket of the target gene T, and outputting the Markov blanket as a cancer driver gene set.

The present invention provides a cell-specific miRNA causal regulation network reasoning method, device, electronic device and storage medium, and relates to the field of gene identification technology. The cell-specific miRNA causal regulation network reasoning method includes: obtaining dual expression spectrum data of miRNA and target gene of target single cell. According to the dual expression spectrum data of miRNA and target gene of target single cell, a CPDAG graph is obtained, and the CPDAG graph includes multiple edges. The differential causal effect between background data and perturbation data of each edge in the CPDAG graph is calculated. According to the edges whose differential causal effects meet preset conditions, the cell-specific miRNA causal regulation network of the target single cell is obtained. An inference method for a cell-specific miRNA causal regulation network is provided, which realizes the construction of a cell-specific miRNA causal regulation network for each single cell, and the obtained cell-specific miRNA causal regulation network can reflect the real miRNA causal regulation mechanism in tissues or cells.

The present invention discloses a method and system for classifying cancer subtypes based on driver gene identification, the method comprising the following steps: obtaining and preprocessing a cancer data set; based on the preprocessed cancer data set, identifying driver genes to obtain global driver genes; based on the global driver genes and a one-to-one multi-classification support vector machine algorithm, constructing a DriverSub-SVM cancer subtype classification model; the DriverSub-SVM cancer subtype classification model is used to classify cancer subtypes. The present invention contributes to the development of precision medicine by constructing an interpretable cancer subtype classification model, and lays the foundation for future cancer subtype classification research.

The invention belongs to the technical field of bioinformatics and genomics, and discloses a spatial multi-omics enhancer and gene regulation relationship identification method, which comprises the following steps of: acquiring spatial multi-omics data of the same tissue slice, preprocessing the data, constructing a layered linear space model according to the preprocessed data, and constructing an enhancer and gene regulation relationship between the enhancer and the gene regulation relationship between the enhancer and the gene regulation relationship between the enhancer and the gene. Removing spatial autocorrelation based on a hierarchical linear space model, then performing parameter estimation of the model, finally performing significance test according to a parameter estimation result to obtain a gene significantly regulated by an enhancer, and identifying a gene and enhancer pair having a significant regulation relationship based on the gene; the identification method disclosed by the invention can accurately and reliably identify the regulation and control relationship between the real enhancer and the gene in the spatial multi-omics.

A traditional Chinese medicine intelligent screening method based on network targeting comprehensive indexes belongs to the technical field of traditional Chinese medicine intelligent screening. The method comprises the following steps: constructing a target set A and a protein interaction network database corresponding to each traditional Chinese medicine; constructing a disease core gene set B based on a protein interaction network; calculating a network targeting comprehensive index based on the target point set A and the disease core gene set B; calculating the network targeting comprehensive index of each traditional Chinese medicine and then performing ascending sorting, wherein the smaller the network targeting comprehensive index is, the stronger the network targeting relevance between the traditional Chinese medicine and the disease is; and matching the traditional Chinese medicines according to the obtained ascending order, and generating a structured and explainable final recommendation report. According to the method, the disease-related gene identification accuracy is improved, the biological interpretation of the result is enhanced, the standardized processing of integrating multi-source data into intelligent recommendation is realized, and the urgent demand of efficient and accurate screening in modern research of traditional Chinese medicines is met.

This application relates to the field of gene recognition technology, and provides a method and related equipment for identifying pathogenic genes based on abnormal expression. The method includes: for each target gene, calculating tail enrichment statistics in two tail directions based on the grouping tag values ​​of all signal vectors to characterize the abnormal expression of the target gene at the low or high expression end; determining the optimal tail enrichment direction from the two tail directions based on the two tail enrichment statistics; calculating the tail boundary shift degree of the target gene according to the optimal tail enrichment direction; calculating the significance p-value of each target gene; identifying multiple pathogenic genes from all target genes based on all significant p-values; and performing pathogenicity analysis based on the tail boundary shift degree and optimal tail enrichment direction of all pathogenic genes to obtain the pathogenic gene identification result. The method of this application can improve the accuracy of pathogenic gene identification.

The invention provides a biological classification character related gene identification method, device and equipment, and relates to the technical field of biological gene identification, and the method comprises the following steps: obtaining gene sequence data and image data of a target organism; a classification model is constructed, a bilinear convolutional neural network is adopted to perform feature extraction on image data to obtain macroscopic image features, a hierarchical attention mechanism model is applied to gene sequence data, and a word-level and sentence-level attention mechanism is used to extract microscopic gene features in a gene sequence; the macroscopic image features and the microscopic gene features are fused, and an incidence matrix is generated through an attention mechanism; performing convolution pooling processing on the incidence matrix through a convolutional neural network, extracting joint features, and completing a species classification task by using a multi-layer perceptron; and outputting a final species classification result, and identifying a gene most related to species classification. According to the method, the genetic characteristics of the organism can be more comprehensively understood, and the model is helped to more accurately position and analyze key genes.

A method of identifying a causal relationship between a drug and an unrelated disease, the method comprising: obtaining genetic instruments for therapeutic targets of the drug; identifying genes (or alleles, or variants thereof) associated with the genetic instruments; obtaining effect sizes of genetic instruments for targets associated with the unrelated disease; identifying genes (or alleles, or variants thereof) associated with the genetic instruments; harmonising the genes obtained previously and the genes identified previously; identifying overlapping genes in the harmonised genes obtained previously; and performing two-sample Mendelian randomization using the overlapping genes identified in step e), wherein the results obtained from the two-sample Mendelian randomization identify the causal relationship, if present.

The present invention discloses a gene identification method based on a masked graph autoencoder, which comprises the following steps: 1. obtaining gene interaction data and omics feature data and performing preprocessing; 2. masking the processed data, specifically including two branches, a node masking module and an edge masking module; 3. inputting the masked network into a graph autoencoder for training, which learns the network embedding representation by reconstructing the nodes and edges of the network; 4. obtaining a low-dimensional embedding of features through the trained encoder, and finally using a logistic regression classifier to classify genes. The present invention simultaneously focuses on the node information and structural information of the graph by masking the nodes and edges in the network respectively, and reduces the dependence of the feature training model on label information in a self-supervised learning manner, thereby accurately classifying genes.

The invention belongs to the technical field of microbiological detection, and particularly relates to a rapid detection method of pseudomonas aeruginosa RAA-CRISPR / Cas12a and application of the rapid detection method of pseudomonas aeruginosa RAA-CRISPR / Cas12a. According to the invention, based on the combination of recombinase polymerase amplification (RAA) and a CRISPR-Cas12a (clustered regularly interspaced short palindromic repeats-CRISPR-Cas12a) system, the ultrasensitive and rapid detection of the Pseudomonas aeruginosa is realized through constant-temperature nucleic acid amplification and specific gene recognition. The detection limit of the method for detecting the pseudomonas aeruginosa is 5.37 * 10 < 1 > copies / L (genome DNA) or 6.4 * 10 < 1 > CFU / mL (bacterial liquid); the detection time is only 21 minutes, and the method can be applied to rapid detection of various environmental samples.

The method and device for identifying spatial feature genes, the electronic device and the storage medium provided by the present disclosure filter genes with average expression lower than a predetermined expression threshold in a first spatial region; a second spatial region for identifying spatial feature genes is demarcated from the first spatial region after filtering genes according to needs; a Moran index is calculated for each gene in the second spatial region; and spatial feature genes in the second spatial region are identified according to the Moran index. Compared with the related art, the present disclosure filters genes with average expression lower than a predetermined expression threshold to obtain suitable genes for research, then demarcates a spatial region of interest for identifying spatial feature genes according to needs, and then calculates a Moran index for each gene in the spatial region of interest to obtain spatial feature genes. The spatial feature genes can be widely applied to spatial group data with single-cell precision, and are more flexible and applicable.

The invention discloses a human organ aging evaluation and analysis method, equipment, a medium and a product, and relates to the field of aging biology and bioinformatics, the method comprises the following steps: based on multi-time-point transcriptome data, carrying out gene identification and screening to obtain aging trend genes; on the cross-organ level, the aging trend genes in different organs are subjected to confluence analysis, and the genes showing consistent up-regulation or consistent down-regulation trends in at least n organs are extracted as global aging trend genes; obtaining an organ aging original score based on the global aging trend gene by adopting the constructed multi-organ aging evaluation model; performing standardization processing on the organ aging original score; and transversely comparing standardized global aging trend gene scores of different organs at the same time point to obtain an aging degree quantitative evaluation result of each organ. The molecular regulation rule of organ aging can be deeply disclosed, and system integration and application of multi-omics data in aging biological research are promoted.

This invention provides peptides with anti-obesity and / or anti-diabetic activity, composed of the amino acid sequence of Sequence 1 or Sequence 2. These peptides inhibit fat accumulation, reduce the size of adipocytes, and increase the expression of phosphorylated hormone-sensitive lipase, adenosine monophosphate-activated protein kinase-α1, and comparative gene recognition-58 (CGM-58), which are lipolysis factors, thereby breaking down accumulated fat. This not only exhibits excellent anti-obesity effects but also effectively reduces blood glucose levels, increasing the expression of adiponectin and adenosine monophosphate-activated protein kinase (CGM-58), which improve insulin resistance; increasing the expression of glucose transporter protein 4 (a glucose transport channel); and increasing the expression of insulin receptor substrate-1 (an insulin receptor signaling protein), thus exhibiting excellent effects against diabetes. The peptides of this invention can be effectively used in the prevention or treatment of obesity and / or diabetes.

The application discloses a sequencing analysis method for single-cell transcriptome based on ONT sequencing and relates to the technical field of gene sequencing analysis.The method comprises the following steps: preparing and constructing a cDNA library of a sample to be measured, sequencing, obtaining three generations of original data, performing quality control on the three generations of original data, obtaining three generations of effective data, performing functional annotation on a reference genome, using software ONT-SC-pipeline to perform data splitting, alignment and quantification, obtaining quantitative data, sequentially performing basic analysis, Marker gene enrichment analysis and advanced analysis on the quantitative data, wherein the basic analysis comprises top gene distribution analysis, hyper-variable gene analysis, clustering and grouping analysis, correlation analysis of each cell group, dimension reduction analysis and Marker gene identification; and the advanced analysis comprises cell annotation analysis, cell communication analysis, cell trajectory analysis, variable splicing analysis and fusion gene analysis.

To provide a new gene identification method capable of identifying genes involved in disease, a gene identification device and use thereof.SOLUTION: A gene identification method comprises an extraction step (S1) for extracting a plurality of pieces of data of interest that satisfies a predetermined condition from expression level data on an expression level of each gene contained in a biometric sample collected from each of a plurality of subjects each suffering from disease, a selection step (S2) for carrying out at least two or more steps capable of selecting a candidate gene, and an identification step (S3) for identifying the candidate gene selected in any of the two or more steps as a gene involved in the disease.SELECTED DRAWING: Figure 1