67 results about "Phenotype correlation" patented technology

The application includes systems and methods for phenotyping. A request is received to identify a target population having a phenotype. Predefined characteristics associated with the phenotype are obtained. Using a retriever model, a set of subjects is identified as potential members of the target population by searching databases. Medical information is obtained by searching a second set of one or more databases and providing corresponding information to an artificial intelligence (AI) component. Natural language instructions are provided to the AI component to provide context to the AI component to determine whether a respective subject in the set of subjects has at least one of the one or more predefined characteristics. A subset of subjects is identified by the AI system to be, or to have a high likelihood of being, a member of the target population.

The invention provides a corn genotype and environment cross-modal feature fusion genome prediction method and model, a deep learning method is adopted to model a complex nonlinear transformation relationship between different omics data including a genome and an environment group, and a GECFGP model is constructed to fuse corn cross-genotype and environment interaction features. Time sequence characteristics and important growth periods related to phenotypes are well mined, and the function of predicting the phenotypes of the complex agronomic traits of the corn is achieved. The method shows high robustness in different experimental settings, has superiority in genome prediction of G * E interaction, explores phenotype-related important growth periods and performance of predicting phenotypes across different years, provides a new thought for a genome prediction analysis method, and has wide application prospects. Therefore, theoretical and application value reference is provided for adaptability analysis of corn in different environments and exploration of key growth periods, and development of the field of intelligent breeding is accelerated.

The invention belongs to the technical field of gene breeding, and discloses a rapid matching breeding improved parent composition, an obtaining method and application. According to the method, a basic SNP marker is obtained; carrying out GWAS analysis on the basis of the collected phenotypes, carrying out dominant genotype identification on markers associated with the phenotypes, and constructing a dominant genotype library of the functional sites; calculating the contribution degree PVE of the functional sites to phenotypes, analyzing the contribution degree PVE, calculating the linkage region Block of each material or group, and constructing a material PVE score library and a Block library; and matching and combining every two materials based on dominant genotypes, PVE of functional sites and linkage regions Blocks of the materials / groups to obtain an optimal combination parent of each material. According to the invention, more favorable genotype polymerization states can be realized.

This application is directed to multiomic and biomimetic digital twin techniques for identifying disease-specific genetic variants. A computer system obtains information of subject genetic variants that are identified from a plurality of biological samples of a plurality of patients who are diagnosed with a target disease. A subset of subject genetic variants are selected based on a plurality of subject phenotypes of the target disease. The subset of subject genetic variants are ranked based on the plurality of subject phenotypes to generate subject genetic variant information. The computer system further obtains subject medical information of the plurality of patients. The computer system applies a biomimetic information model to process the subject genetic variant information, the subject medical information, and the general genetic variant information of the target disease and identify a set of target genetic variants associated with the phenotype of the disease(s) satisfying a variant selection criterion.

To provide a technique that enables direct seeding cultivation of seed potatoes in the field of production of sweet potato tuberous roots (sweet potato cultivation), which offers significant labor-saving benefits, wherein the technique enables efficient development of sweet potato plants having desired traits and exhibiting low enlargement of seed potatoes.SOLUTION: A method for identifying a sweet potato plant exhibiting low enlargement of seed potatoes, includes the steps of: detecting a nucleotide site indicating the "Kyushu 199-type variant allele" associated with a DNA marker from a genomic DNA of the sweet potato plant to be identified, with respect to "the DNA marker that shows relevance to the phenotype of low enlargement of seed potatoes" in the genomic DNA of sweet potato plants; and determining that the sweet potato plant to be identified is a sweet potato plant exhibiting low enlargement of seed potatoes if a nucleotide site indicating the "Kyushu 199-type variant allele" associated with the DNA marker is detected in the detection step.SELECTED DRAWING: Figure 2

The invention provides a method based on Pearson's collinearity selection (PCS). The method is used for screening features closely related to a target phenotype from high-dimensional genome data. According to the PCS method, by calculating Pearson's correlation coefficients among the features, redundant features are effectively removed, the multi-collinearity problem is reduced, the quality of feature selection is remarkably improved, and the stability of model prediction is enhanced. The method is particularly suitable for the fields of genotype and phenotype correlation analysis and the like, and can optimize the data processing and analysis process. In addition, the PCS method has high cross-population adaptability. Feature selection and prediction verification are carried out on data sets of multiple populations, it is proved that multiple models can keep consistent good prediction performance among different data sets by using the PCS method, and the excellent generalization ability is shown. The method not only can effectively predict the phenotype of a single population, but also can be successfully applied to prediction tasks of other populations, and the application range of the method in the fields of precision medicine, crop breeding and the like is further widened. Compared with a traditional feature selection method, the PCS method has the advantages that feature redundancy is reduced, meanwhile, features highly related to the target phenotype can be better reserved, the prediction precision of the model is remarkably improved, and more efficient and reliable technical support is provided for genome data analysis.

The invention discloses a scophthalmus maximus anti-edwardsiella tarda SNP molecular marker and application thereof, and belongs to the technical field of molecular breeding and biology. The nucleotide sequence of the SNP molecular marker is shown as SEQ ID No.1, and the polymorphic site of the SNP molecular marker is A / C. The invention further provides application of the SNP molecular marker in screening of turbots with the Edwardsiella tarda resistant character, and the CC genotype turbots are individuals with the Edwardsiella tarda resistant character. By utilizing the SNP molecular marker provided by the invention, DNA level selection can be carried out in the early stage of fish fries, interference of environmental factors is avoided, the accuracy and efficiency of breeding are remarkably improved, and the breeding period is shortened. The molecular marker is remarkably verified in an independent verification group through linear regression analysis, genotype-phenotype association is stable and reliable, false positive is eliminated, and the molecular marker has a good market application prospect.

The invention belongs to the field of bioinformatics, and particularly relates to a quantitative character multi-site oscillation search whole genome association analysis system and method. The invention provides a method for performing whole genome association analysis by using a quantitative character multi-site oscillation search whole genome association analysis system, the method aims to find out SNP sites, namely PseQTNs, related to specific phenotypes, and the method comprises the following steps: S1, data preparation: preparing an SNP matrix file and a phenotype matrix file; s2, data preprocessing: de-duplicating the input SNP matrix, standardizing the matrix according to columns, and standardizing a phenotype matrix according to columns; and S3, correlation analysis: screening out phenotype-related SNP sites, namely PseQTNs, from the SNP matrix through an oscillation search strategy. The method has important and practical application value in the aspects of animal and plant important character related gene mining, human complex disease related gene mining and the like.

The invention discloses application of excellent natural variation of a GmPOD123 gene in drought resistance regulation and control of a soybean germination period, and relates to the technical field of biology. According to the application disclosed by the invention, the GmPOD123 gene is subjected to germination period drought resistance phenotype correlation analysis in 400 parts of soybean germplasm resource populations, so that the GmPOD123 gene is excavated and verified to have excellent natural variation which is remarkably related to the soybean germination period drought resistance. A further research result shows that a transgenic line of the over-expression GmPOD123HapG (HapG, Chr17: 14532734) gene can promote soybean germination under drought stress, and it is proved that the gene can improve the drought resistance of the soybean in the germination period. Therefore, the peroxidase gene GmPOD123 disclosed by the invention not only enriches candidate gene resources for improving the drought resistance of the soybean in the germination period, but also provides a candidate molecular marker for cultivating new germplasm of the drought-resistant soybean.

The invention discloses a phenotype-based gene mutation analysis method, system and device and a computer readable storage medium, and the method comprises the following steps: data acquisition: acquiring phenotype and gene mutation data of a subject; phenotype description: describing the phenotype of the subject as a standard entry; gene assignment: assigning each gene of gene mutation of the subject according to genetic heterogeneity, genetic pattern and phenotype comparison to obtain a score of each gene; and sorting: sorting the gene mutation of the subject based on the score. According to the application, the phenotype correlation between the phenotype of the subject and the gene where the mutation is located is analyzed, the gene mutation is assigned according to the correlation, and then the mutation is sorted according to the score, so that medical interpretation personnel can preferentially pay attention to the pathogenic gene mutation possibly related to the phenotype of the subject; and the working efficiency of medical interpretation personnel can be improved.

The present disclosure relates to a discovery platform including machine-learning techniques for using medical imaging data to study a phenotype of interest, such as complex diseases with weak or unknown genetic drivers. An exemplary method identifying a covariant of interest with respect to drug response phenotype (DRP) of a treatment is disclosed.

The invention discloses a Chinese rose petal outer edge notch whole genome SNP molecular marker combination and application thereof, and aims to overcome the defects in the prior art, 879 SNP markers significantly related to petal outer edge notch are identified by fusing 358 Chinese rose whole genome re-sequencing data and petal outer edge notch phenotype correlation analysis and applying a multi-model combined screening technology, and the SNP markers are used for identifying the petal outer edge notch. And a whole genome selection model with high prediction accuracy is established. The markers are distributed on 14 chromosomes, high-throughput and rapid detection can be achieved, the detection period is shortened, the petal wave character is directly predicted through seedling genotype data, and the breeding process is accelerated. The method has the advantages of low cost, high throughput and industrialization potential, provides key data support for Chinese rose petal outer edge notch genetic law analysis and molecular marker assisted breeding, and significantly improves the breeding efficiency and precision.

The application discloses a high-throughput KASP molecular marker significantly related to plant height of Gossypium hirsutum and application, and belongs to the technical field of cotton molecular breeding. The KASP marker is successfully developed based on cotton plant type related genes and resequencing data of germplasm resources. Through genotyping and phenotype correlation analysis in 246 natural Gossypium hirsutum populations, four KASP markers (PH-TK1-PH-TK4) significantly related to plant height in three environments are screened. The regulation effect and aggregation effect of the markers on plant height are verified in a separation population. Experiments prove that the KASP marker can be used for high-throughput and accurate genotyping of plant height in early cotton breeding, and provides an effective tool for molecular marker assisted selection of cotton ideal plant type, and helps to accelerate the breeding process of cotton varieties suitable for mechanical harvesting.

The present invention relates to a multi-trait composite breeding method for fish feed conversion rate, growth and survival rate, belonging to the field of genetic breeding of aquatic animals. The method establishes composite traits by using phenotypic correlations, then establishes genomic selection methods for multiple traits, and finally uses genetic correlations to establish a comprehensive genomic breeding value calculation method and a parental selection scheme. The method of the present invention overcomes the problem of excessive subjectivity in artificially setting weighting parameters in traditional methods. Through the comprehensive evaluation of multiple important economic traits, it not only improves the objectivity and accuracy of breeding selection, but also enables more effective resource allocation and higher genetic gain in multi-trait breeding, which is beneficial to the sustainable development of aquaculture.

The present invention provides a method for evaluating the risk of developing a hereditary disease in an individual by a variant of uncertain clinical significance (VUS), the method comprising: introducing DNA for a genetic factor for a hereditary disease, including at least one VUS, into a fertilized egg of zebrafish under the control of a promoter of a gene involved in a phenotype; and evaluating the risk of developing the hereditary disease by the VUS, by observing the change in the phenotype in the zebrafish.

The application provides a kind of genotypes and phenotypic association analysis method based on multiple phenotypes, belongs to the technical field of bioinformatics, and establishes SNP-gene-phenotype three-layer network to explore the relationship between genotypes and phenotypes using statistical data, and the internal correlation between genomics and genomics, such as genes and genes, and phenotypes and phenotypes, is considered in the three-layer network construction process, which is more consistent with biological reality;Solve the problem that clinical data is difficult to obtain and SNP and phenotype correlation cannot be predicted;The biological pathway correlation between SNP-gene-phenotype different omics layers is analyzed.Through the analysis of various database data, it is found that the internal correlation of each database omics has quantitative value, while the correlation between omics generally has qualitative value, i.e. the correlation is 1 and the non-correlation is 0.Through the establishment of three-layer model, the key correlation genes can be predicted using the quantitative value of the internal omics and the qualitative value of the inter-omeric relationship, and the pathway relationship score between each layer is calculated to analyze the pathway relationship between SNP-gene-phenotype.

The invention discloses a carbapenem drug resistance marker screening method and system based on a cross-species compressed Debrueine diagram and a medium. The method comprises the following steps: starting from whole genome sequencing data of gram-negative bacteria belonging to different species and carbapenem drug phenotypes of the gram-negative bacteria, constructing a compressed Debrueine graph based on cross-species joint data, and taking existence / deletion of nodes in the graph as unified genetic variation characteristics. Performing correlation analysis on the nodes and the drug resistance phenotypes by using a linear hybrid model to obtain a candidate node set related to the phenotypes; k-mer is extracted based on the candidate node sequence, and secondary statistical screening is completed in combination with chi-square test and mutual information; and finally determining a group of carbapenem drug-resistant genetic markers which can be applicable across species through a hierarchical feature selection strategy of random forest and XGBoost. Efficient dimension reduction of large-scale cross-species genome data, cross-species consistent variation representation and high-interpretability marker screening are achieved.

The invention relates to the technical field of molecular biology and plant breeding, and provides a black cabbage yellowing phenotype related KASP molecular marker, the marker corresponds to a specific SNP site of a black cabbage BcCHL27 gene, the SNP site is located at the 217bp position of the black cabbage BcCHL27 gene, and the basic group is mutated from A to G. The invention also provides a primer group for detecting the molecular marker. Meanwhile, the invention also provides application of the molecular marker or the primer group. Based on the KASP molecular marker, genotype identification and character screening of the yellow phenotype of the black cabbage are carried out, the accuracy, specificity and screening efficiency of yellow character detection of the black cabbage can be remarkably improved, rapid high-throughput identification of the yellow phenotype of the black cabbage in the seedling stage is achieved, an efficient tool is provided for genetic research of the yellow character of the black cabbage and molecular assisted breeding, and the application prospect is wide. Therefore, breeding cycle is shortened, resources are saved, and genetic modification of black cabbage is accelerated.

The invention provides a causal inference method and device based on genetic variation, electronic equipment and a medium. The causal inference method comprises the following steps: acquiring whole genome SNP data and proteome data of a detection sample of a target population from a detection platform; performing whole genome association analysis on the whole genome SNP data to obtain outcome SNP data associated with the target phenotype; performing protein quantitative trait site analysis based on the whole genome SNP data and the proteome data to obtain exposure SNP data associated with the target exposure factor; and performing data preprocessing on the outcome SNP data and the exposed SNP data, and performing Mendel randomization analysis on the preprocessed outcome SNP data and exposed SNP data to obtain a causal relationship between the target exposure factor and the target phenotype. According to the method, the accuracy and reliability of Mendel stochastic analysis results are improved.

The present invention provides a genomic prediction method and model that integrates cross-modal features of corn genotypes and environments. It uses deep learning methods to model the complex nonlinear transformation relationship between different omics data, including genome and environment groups. By constructing a GECF_GP model to integrate the characteristics of corn cross-genotype and environment interactions, it better explores temporal characteristics and important growth periods related to phenotypes, and realizes the function of predicting the phenotypes of complex agronomic traits of corn. The present invention demonstrates strong robustness in different experimental settings, has superiority in genomic prediction of G×E interactions, explores important growth periods related to phenotypes and the performance of predicting phenotypes across different years, and provides new ideas for genomic prediction and analysis methods. It further provides theoretical and application value references for the adaptability analysis of corn in different environments and the exploration of key growth periods, accelerating the development of the field of smart breeding.

The invention discloses a disease community discovery and patient layering method based on gene information guidance, and belongs to the technical field of biological information.According to the method, an initial disease set is created through full phenotype correlation research, and the diseases are expressed in a high-dimensional embedding space of electronic health record data; vector representation of diseases is optimized and extracted by learning individual features of the diseases and a topological structure of a co-disease network of the diseases through a graph auto-encoder, the diseases are divided into different communities with consistent interiors by embedding vectors into the optimized diseases and applying a clustering algorithm, and the method is provided based on the individual health history, and is suitable for the individual health history and the co-disease network. According to the method for classifying new patients into a certain community, accurate patient layering is achieved, a complex multi-disease network is disclosed, a brand-new systematic perspective is provided for disease understanding, a traceable and data-driven bridge from genes to patient subgroups is established, and a novel framework is provided for medical conversion.

A method for capturing distinct higher order interactions of a dataset relevant to biological inferences includes deriving Hamiltonian parameters for the dataset, wherein the dataset includes data responsive to a phenotype of interest. The method further includes generating a partition function responsive to the Hamiltonian parameters; calculating cumulant moments of the partition function and deriving higher order cumulants using the Hamiltonian parameters, wherein the higher order cumulants are responsive to the phenotype of interest.

The invention relates to a fish feed conversion rate, growth rate and survival rate multi-character composite breeding method, and belongs to the field of aquatic animal genetic breeding. According to the method, composite characters are established by using phenotypic correlation, and then a genome selection method of multiple characters is established; and finally, establishing a comprehensive genome breeding value calculation method and a parent selection scheme by utilizing genetic correlation. According to the method, the problem that in a traditional method, the subjectivity of manually setting weighting parameters is too high is solved. Through comprehensive evaluation of a plurality of important economic characters, not only are the objectivity and accuracy of breeding selection improved, but also more effective resource allocation and higher genetic gain can be realized in multi-character breeding, and sustainable development of aquaculture is facilitated.

The present application relates to a kind of detection methods of strong putrefaction Pseudomonas lundensis, comprising extracting nucleic acid in the sample to be measured;Then with nucleic acid as template, using specific RAA primer pair is recombined enzyme-mediated amplification, and obtains amplification product;RAA primer pair targets the aprX gene of Pseudomonas lundensis comprising single nucleotide polymorphism site associated with strong putrefaction phenotype;Finally, the amplification product is added to CRISPR / Cas detection system, incubated at constant temperature, then real-time detects its fluorescence signal;Detection system includes Cas protein, specific crRNA and fluorescent reporter probe, and crRNA can recognize single nucleotide polymorphism site associated with strong putrefaction phenotype in Pseudomonas lundensis aprX gene.The present application improves the detection accuracy, and detection process is simple to operate, and reaction system is simple, not only improves detection efficiency, also reduces detection cost.

The invention discloses application of a mutant gene related to ASD, the mutant gene is at least one of the following genes: RAB5A, EFNB2, NEDD9, WNT9B, LIN7C, FOLR1, BNIP3, CAMSAP2, ADCY10, FCSK, UGDH and PIGH, the genes are related to ASD phenotype related pathways, but the genes are not recorded in an SFARI / HPO database before. The findings are beneficial to clarification of genetic causes of ASD and provide favorable help for early diagnosis and treatment research of ASD.

The invention discloses a special scientific research disease library system based on hepatolenticular degeneration, which belongs to the field of medical information technology and clinical scientific research and comprises a case data acquisition module, a database management module, a clinical scientific research analysis module, a follow-up management module and a standardized quality control module. Structured input of copper metabolism indexes, clinical manifestation, genetic information and treatment data is realized through a disease specificity data acquisition template; semantic fusion and efficient retrieval of multi-source heterogeneous data are completed by utilizing a database architecture based on a knowledge graph; a copper metabolism kinetic model, a nervous system curative effect evaluation model and a genotype-phenotype association model are embedded, so that automatic analysis of scientific research data is realized; and generating a personalized follow-up plan and dynamically adjusting the follow-up period in combination with a rule engine. The system realizes data acquisition standardization, scientific research analysis intelligence and follow-up management systematization, and is suitable for high-quality accumulation and comprehensive research of hepatolenticular degeneration scientific research data.

The present disclosure provides a neural document embedding based ontology mapping. Conventional methods that map ontology concepts across domains / species extensively take help of bridging ontologies. Initially the system receives a Human Phenotype (HP) Identification number (ID) pertaining to a phenotype. A first HP ID vector is computed from the HP ID using a trained word2vec model. A second HP ID vector is computed from the HP ID using a trained Doc2vec model. An average HP ID vector is computed based on the first HP ID vector and the second HP ID vector. A plurality of cosine similarity scores are computed based on a comparison between the average HP ID vector and a plurality of average MP ID vectors. The plurality of MP IDs are sorted based on the plurality of cosine similarity scores. The plurality of MP IDs corresponding to the HP ID are selected based on a selection threshold.

The invention discloses a gene variation frequency calculation system based on personalized phenotypic crowds. The gene variation frequency calculation system comprises a data acquisition module, a clinical phenotypic category module, a crowd phenotypic construction module, a data classification module, a data extraction module, a personalized combination module and a mark calculation module. According to the method, gene variation population frequency related to a specific phenotype can be provided, gene variation frequency data rich in phenotype can be provided by combining the genotype with the specific phenotype, reference result data of genome variation frequency of different populations can be obtained through calculation, the data can be stored and transmitted, and an existing database can be perfected.

The present disclosure relates to systems and methods for identifying genes associated with phenotypes. A list of phenotypes is provided as input and the systems and methods automatically provide an output with a list of genes associated with the phenotypes provided. The systems and method analyze assertions linking a gene to a phenotype using a graph-based algorithm to identify the genes associated with the phenotypes.

The application discloses a transcription factor regulation network modeling method based on single cell transcriptome data, comprising the following steps: S1, extracting multi-phenotype single cell omics data, performing data cleaning, and integrating the cleaned data; S2, based on a biological knowledge base, analyzing the data processed in S1, and constructing a prior inter-molecular interaction relationship network; S3, based on a multi-factor non-negative matrix factorization algorithm, establishing a multi-dimensional molecular synergistic interaction relationship module according to the data processed in S1 and the prior inter-molecular interaction relationship network in S2; S4, calculating the interaction relationship module related to the phenotype of the multi-dimensional molecular synergistic interaction relationship module; and S5, visualizing and exporting the interaction relationship between the multi-dimensional molecular synergistic interaction relationship module and the phenotype. With the aid of single cell sequencing technology and prior biological knowledge, the application extracts multi-dimensional molecular synergistic regulation relationship in high-dimensional single cell omics data, and obtains a transcription factor-gene function synergistic regulation interaction mechanism closely related to phenotype changes.