7 results about "Reference population" patented technology

The method for property typicality determination can include: determining a property, determining attribute values for the property, determining a reference population for the property, determining reference population attribute values, determining a typicality metric for the property, and optionally determining an influential attribute.

The application discloses a biological age evaluation method and system based on multi-modal clinical data, wherein the evaluation method comprises the following steps: step 1, data acquisition and multi-modal construction; step 2, data preprocessing and feature expression; step 3, reference population construction and feature screening; step 4, biological age prediction model training and verification; step 5, biological age acceleration index calculation; and step 6, standardization and risk assessment output. By fusing heart ultrasound examination data and blood biomarker data, the application realizes comprehensive characterization of the aging state of an individual, further calculates a biological age acceleration index, and uses the biological age acceleration index for cardiovascular disease risk assessment, so as to solve the problems that a single-modal biological age model in the prior art cannot comprehensively depict the aging process and cannot simultaneously reflect changes in organ function and changes in systemic physiological state.

The invention discloses an individual dynamic metabolic abnormality early warning system based on function type data analysis, which can judge whether the dynamic metabolic condition of an individual to be evaluated is abnormal or not according to the fasting metabolic response data of the individual and a healthy reference population and the metabolic response data after a tolerance test, and carries out early warning if the dynamic metabolic condition exists. The system comprises a data input module, a data analysis module and an early warning module, the data input module is responsible for data input, and input data comprises index data of to-be-evaluated individuals and health reference people at multiple time points before and after a tolerance test; the data analysis module is responsible for processing and analyzing the data and calculating a metabolism score of the individual to be evaluated; the early warning module is responsible for judging whether metabolism abnormity early warning is carried out or not, comparing the metabolism score obtained by the data analysis module with a threshold value, if the metabolism score is lower than the threshold value, early warning is carried out, and otherwise, early warning is not carried out.

The invention relates to the technical field of genome information analysis, in particular to a local pig optimal heterosis prediction method based on genome information. According to the method, pure parents with clear genetic backgrounds are accurately identified and screened out by utilizing high-density SNP marker information covering a whole genome, a reference population is constructed, large-scale hybridization propagation is simulated, structured virtual population data is formed, and multi-character phenotype data of filial generations are standardized and weighted, so that the genetic backgrounds of the filial generations are optimized, and the genetic backgrounds of the filial generations are optimized. The method comprises the following steps of: dividing a plurality of characteristic groups such as a production performance type and a reproductive performance type, and analyzing the correlation between phenotypic deviation and genotype in each group, so as to separate and quantify dominant effect caused by heterozygous genotype and recessive effect caused by homozygous genotype, construct explicit and recessive weight parameters, and determine the genetic relationship between the dominant effect and the recessive effect; finally, the non-additive effect parameters are superposed to a traditional additive breeding value model, direct quantitative prediction of heterosis is achieved, and therefore the accuracy and efficiency of hybrid combination matching are greatly improved.

The application relates to the technical field of genome selection, and provides a multi-omics genome selection method and application thereof in livestock and poultry breeding, which comprises the following steps: obtaining reference population data and target population data and carrying out pretreatment; training an optimal linear unbiased prediction model and an elastic network model by using the reference population data; inputting cis-single nucleotide polymorphism site genotype data of the target population into the trained gene expression prediction model to obtain predicted gene expression of each individual in the target population; estimating the cis-heritability of each gene based on the reference population data, and screening gene prediction expression data greater than a cis-heritability threshold; integrating the screened gene prediction expression data and genome data to construct a multi-omics genome selection model, and obtaining a genome breeding value of each individual based on the multi-omics genome selection model. The application can adapt to genetic regulation structure differences of different genes and obtain high-quality prediction results.

The application discloses a kind of paternal relationship determination method and device, belong to the field of forensic relationship determination, the method is: obtaining Y-STR genotype data to be determined, calculate its total step difference and difference site number;The difference value is compared with the corresponding threshold value to determine whether it belongs to the same paternal line;Wherein, the threshold value is based on reference population Y-STR database, by paternal lineage simulation method to generate simulation data, and is determined by using kernel density estimation method.Therefore, by implementing the application, the accuracy of Y-STR paternal relationship determination can be improved.

The method for property typicality determination can include: determining a property, determining attribute values for the property, determining a reference population for the property, determining reference population attribute values, determining a typicality metric for the property, and optionally determining an influential attribute.