Human body period expression gene identification method based on RNN-CNN neural network fusion algorithm

Abstract

The invention discloses a human body period expression gene identification method based on an RNN-CNN neural network fusion algorithm. The method comprises the steps of performing missing filling andtime sequence period extension on original data, then establishing a high-quality learning dataset through a data level, performing thermal graph clustering on period expression data for preliminarilyunderstanding the specific expression of a biological clock gene in a visual sense angle, then combining a recurrent neural network algorithm and a convolutional neural network algorithm for obtaining an RNN-CNN fused algorithm by means of a deep learning algorithm, and performing periodical and non-periodical subtype classification on an RNN-CNN fused algorithm classifier and common deep learning algorithms CNN and RNN, performing cross validation on a classifying result, and performing evaluation by means of an accuracy score, a recall rate and a comprehensive evaluation index.

Description

technical field [0001] The invention belongs to the field of gene information processing, and in particular relates to a human body cycle expression gene identification method based on an RNN-CNN neural network fusion algorithm. Background technique [0002] The biological clock is also called the biological clock. It is an invisible "clock" in the organism, which is actually the internal rhythm of the life activities of the organism, which is determined by the time structure sequence in the organism and the specific periodic expression of the biological clock gene. Studies have shown that there are circadian rhythm changes in body temperature, pulse, blood pressure, oxygen consumption, and hormone secretion levels. The biological clock relies on the reciprocating oscillation like a clock, and its existence has extremely important biological significance. It controls the three rhythms of people, namely intelligence, physical strength, and emotion. Periodic rhythms enable o...

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Problems solved by technology

[0005] The purpose of the present invention is to provide a human body periodic expression gene identification method based on the RNN-CNN neural network fusion algorithm for the problems of high dimensionality, low sample size, and data imbalance in the gene expression profile data set, which is an improved Limitations of existing algorithms, RNN-CNN fusion algorithm gene recognition method aimed at improving the accuracy of gene recognition

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