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Identification of Cell Cycle-Regulated Genes by Convolutional Neural Network


Chenglin Liu, Peng Cui and Tao Huang   Pages 1 - 9 ( 9 )


Background: The cell cycle-regulated genes express periodically with the cell cycle stages, and the identification and study of these genes can provide a deep understanding of the cell cycle process. Large false positives and low overlaps are big problems in cell cycle-regulated gene detection. Methods: Here, a computational framework called DLGene was proposed for cell cycle-regulated gene detection. It is based on the convolutional neural network, a deep learning algorithm representing raw form of data pattern without assumption of their distribution. First, the expression data was transformed to categorical state data to denote the changing state of gene expression, and four different expression patterns were revealed for the reported cell cycle-regulated genes. Then, DLGene was applied to discriminate the non-cell cycle gene and the four subtypes of cell cycle genes. Its performances were compared with six traditional machine learning methods. At last, the biological functions of representative cell cycle genes for each subtype were analyzed. Results: Our method showed better and more balanced performance of sensitivity and specificity comparing to other machine learning algorithms. The cell cycle genes had very different expression pattern with non-cell cycle genes and among the cell-cycle genes, there were four subtypes. Our method not only detects the cell cycle genes, but also describes its expression pattern, such as when its highest expression level is reached and how it changes with time. For each type, we analyzed the biological functions of the representative genes and such results provided novel insight of the cell cycle mechanisms.


cell cycle, cell cycle-regulated genes, deep learning, convolutional neural network, machine learning, classification


Shanghai Jiao Tong University, Shanghai Jiao Tong University - School of Life Sciences and Biotechnology & Department of Bioinformatics, SJTU-Yale Joint Center for Biostatistics Shanghai, Shanghai Institutes for Biological Sciences - Institute of Health Sciences Shanghai

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