Discontinuous context modeling and maximum entropy principle based gene compression method

Abstract

The invention provides a discontinuous context modeling and maximum entropy principle based gene compression method. The method includes that in a first stage, two actual situations with reference sequences and without reference sequences are considered simultaneously, and repetitive sequences in or among gene sequences are represented with a dictionary method, so that compression efficiency is improved; in a second stage, a statistical encoder is composed of a predictor and an arithmetic encoder, the predictor introduces a modeling scheme in a discontinuous bit context combination type on the basis of traditional continuous context models for non-repetitive sequences, and prediction probabilities independently generated by all models are merged with a Logistic regression formula generated on the basis of the maximum entropy principle, so that a more accurate final prediction probability value is obtained and put into the arithmetic encoder. By the method, compression efficiency can be improved remarkably, and efficient storage is realized.

Description

technical field [0001] The present invention relates to an information compression method for super-large-scale gene sequences, and specifically relates to a method that combines discontinuous context modeling ideas with traditional continuous context modeling ideas, and uses the principle of maximum entropy to obtain the statistics of the final predicted probability. compression method. Background technique [0002] DNA is an important material basis for the survival, continuation and development of organisms, and has great scientific and social value. At present, DNA research is widely used in many important fields such as biology, medicine, and genetic science, such as protecting endangered biological species by collecting and preserving DNA information, predicting information based on human gene sequences, and finding gene variation rules to treat cancer Tumor etc. Various DNA sequence determination projects that provide basic experimental data for these disciplines ha...

AI Technical Summary

Problems solved by technology

In the process of performing statistical modeling on the sequence to obtain the probability and then using arithmetic coding, almost all compression methods observe the symbol sequence one by one and perform sequential modeling for prediction, and then obtain the final predicted probability through the Bayesian averaging method. Oversimplified predictive models are very detrimental to capturing correlations within gene sequence forms arranged in non-traditional regularities

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