关键词: 转录调控网络, 深度自编码器, XGBoost, 逻辑回归

Abstract:

The transcriptional regulatory network has been a hot research topic in the field of systems biology and bioinformatics. The transcriptional regulatory network provides the necessary means to reveal the mechanism of biochemical reactions within the cell. At present, the research in this field has some problems, such as inadequate utilization of biological data and low precision of gene transcriptional regulatory network, especially in larger data sets. To solve the above problems, this paper involves the gene expression data, gene sequence data and gene annotation data, and proposes DAXL (combined model with XGBoost and logistic regression based on deep autoencoder). The experimental results in Arabidopsis data set show that DAXL method improves the accuracy of predicting transcriptional regulatory network, and has obvious advantages compared with the contrast method.

Key words: transcriptional regulatory network, deep autoencoder, XGBoost, logistic regression