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Deep convolutional neural networks for predicting leukemia-related transcription factor binding sites from DNA sequence data by Jian He; Xuemei Pu; Menglong Li; Chuan Li; Yanzhi Guo is a Chemistry article available to read on EtoBox.

What is Deep convolutional neural networks for predicting leukemia-related transcription factor binding sites from DNA sequence data about?

Transcription factors are proteins that could bind to specific DNA sequences so as to regulate gene expressions. Currently, identification of transcription factor binding sites locating in DNA sequences is very important for building regulatory model in biological systems and identifying pathogenic variations. Traditional machinelearning methods have been successfully used for biological prediction problems based on DNA or protein sequences, but they all need to manually extract numerical features, which is not only tedious, but also would ignore effective information of first-order sequences. In this paper, based on the principle of deep learning (DL), we constructed prediction model for transcription factor binding sites only from DNA original base sequences. Here, a DL method based on convolutional neural network (CNN) and long short-term memory (LSTM) were proposed to investigate four leukemia categories from the perspective of transcription factor binding sites using four large non-redundant datasets for acute, chronic, myeloid and lymphatic leukemia, respectively. Compared with three widely used machine-learning methods of artificial neural network (ANN), support vector machi

Who reads Deep convolutional neural networks for predicting leukemia-related transcription factor binding sites from DNA sequence data?

Author

Jian He; Xuemei Pu; Menglong Li; Chuan Li; Yanzhi Guo

Publisher

Elsevier Science; Elsevier ; Elsevier BV (ISSN 0169-7439)

Published

2020

Language

EN

Field

Chemistry (Physical Sciences)