Ancestor source polymorphism prediction method based on big data artificial intelligence algorithm

Abstract

The invention discloses an ancestor polymorphism prediction method based on a big data artificial intelligence algorithm, which comprises the following steps of: A, constructing a training set according to population genome data; B, performing gene orientation on the group samples of the training set, and performing 1 and -1 encoding on the two haplotypes after orientation, and performing window division on the genome at the same time; c, selecting an optimal classifier through a voting strategy to form a window observation sequence, and taking the result of the classifier as the next step ofinput; d, constructing a transfer matrix and an emission matrix of the window, and establishing a hidden Markov model; and E, predicting probability distribution of a hidden state through the hidden Markov model, solving an optimal ancestor source result label, and outputting the optimal ancestor source result label as a final result. Through the method, the defect that an existing population polymorphism method has an important effect on genetic population research and positioning of some population-associated disease genome fragments can be overcome, and meanwhile the polymorphism of the chromosome genome fragments can be finely predicted.

Description

technical field [0001] The invention relates to the technical field of big data research, in particular to an ancestor polymorphism prediction method based on a big data artificial intelligence algorithm. Background technique [0002] Since humans came out of Africa, each evolution of the human race may carry different gene mutations, and each change of genetic information may cause species diversity. With the continuous reproduction of thousands of generations of humans, there are more and more mutations in the human genome, which may lead to differences among different groups of humans. Predicting the genetic diversity of different populations and accurately positioning and judging the genetic diversity of chromosomal genome segments is beneficial to population research and analysis, as well as the location of disease genes on the genome. [0003] The current population polymorphism prediction algorithm cannot effectively distinguish some genetically similar groups. For e...

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Problems solved by technology

[0004] The purpose of the present invention is to provide an ancestral polymorphism prediction method based on a big data artificial intelligence algorithm, and to predict the ancestral polymorphism by constructing an artificial intelligence algorithm. This method can overcome the disadvantages of existing population polymorphism methods Groups with very similar genetics, such as genetic polymorphisms in China, can also refine the prediction of polymorphisms in chromosome genome segments. This method plays an important role in the study of gene groups and the location of genome segments in certain population-associated diseases

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