RT Journal Article
SR Electronic
T1 DeepDBP: Deep Neural Networks for Identification of DNA-binding Proteins
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 829432
DO 10.1101/829432
A1 Shadman Shadab
A1 Md Tawab Alam Khan
A1 Nazia Afrin Neezi
A1 Sheikh Adilina
A1 Swakkhar Shatabda
YR 2019
UL http://biorxiv.org/content/early/2019/11/22/829432.1.abstract
AB DNA-Binding proteins (DBP) are associated with many cellular level functions which includes but not limited to body’s defense mechanism and oxygen transportation. They bind DNAs and interact with them. In the past DBPs were identified using experimental lab based methods. However, in the recent years researchers are using supervised learning to identify DBPs solely from protein sequences. In this paper, we apply deep learning methods to identify DBPs. We have proposed two different deep learning based methods for identifying DBPs: DeepDBP-ANN and DeepDBP-CNN. DeepDBP-ANN uses a generated set of features trained on traditional neural network and DeepDBP-CNN uses a pre-learned embedding and Convolutional Neural Network. Both of our proposed methods were able to produce state-of-the-art results when tested on standard benchmark datasets.DeepDBP-ANN had a train accuracy of 99.02% and test accuracy of 82.80%.And DeepDBP-CNN though had train accuracy of 94.32%, it excelled at identifying test instances with 84.31% accuracy. All methods are available codes and methods are available for use at: https://github.com/antorkhan/DNABinding.