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Int. J. Mol. Sci. 2015, 16(8), 17315-17330; doi:10.3390/ijms160817315

DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields

1,2,†,* , 3,†
,
2
and
2
1
Department of Human Genetics, University of Chicago, Chicago, IL 60637, USA
2
Toyota Technological Institute at Chicago, Chicago, IL 60637, USA
3
MoE Key Laboratory of Developmental Genetics and Neuropsychiatric Diseases, Bio-X Center, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Lukasz Kurgan and Vladimir N. Uversky
Received: 28 May 2015 / Revised: 15 July 2015 / Accepted: 16 July 2015 / Published: 29 July 2015
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Abstract

Intrinsically disordered proteins or protein regions are involved in key biological processes including regulation of transcription, signal transduction, and alternative splicing. Accurately predicting order/disorder regions ab initio from the protein sequence is a prerequisite step for further analysis of functions and mechanisms for these disordered regions. This work presents a learning method, weighted DeepCNF (Deep Convolutional Neural Fields), to improve the accuracy of order/disorder prediction by exploiting the long-range sequential information and the interdependency between adjacent order/disorder labels and by assigning different weights for each label during training and prediction to solve the label imbalance issue. Evaluated by the CASP9 and CASP10 targets, our method obtains 0.855 and 0.898 AUC values, which are higher than the state-of-the-art single ab initio predictors. View Full-Text
Keywords: intrinsically disordered proteins; prediction of disordered regions; machine learning; deep learning; deep convolutional neural network; conditional neural field intrinsically disordered proteins; prediction of disordered regions; machine learning; deep learning; deep convolutional neural network; conditional neural field
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Wang, S.; Weng, S.; Ma, J.; Tang, Q. DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields. Int. J. Mol. Sci. 2015, 16, 17315-17330.

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