AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields

This article formulates IDR prediction as a sequence labeling problem and employs a new machine learning method called Deep Convolutional Neural Fields (DeepCNF) to solve it. DeepCNF is an integration of deep convolutional neural networks (DCNN) and conditional random fields (CRF); it can model not only complex sequence–structure relationship in a hierarchical manner, but also correlation among adjacent residues. To deal with highly imbalanced order/disorder ratio, instead of training DeepCNF by widely used maximum-likelihood, we develop a novel approach to train it by maximizing area under the ROC curve (AUC), which is an unbiased measure for class-imbalanced data.
Results: Our experimental results show that our IDR prediction method AUCpreD outperforms existing popular disorder predictors. More importantly, AUCpreD works very well even without sequence profile, comparing favorably to or even outperforming many methods using sequence profile. Therefore, our method works for proteome-wide disorder prediction while yielding similar or better accuracy than the others.
Availability and Implementation: http://raptorx2.uchicago.edu/StructurePropertyPred/predict/
Contact: [e-mail], [e-mail]
Supplementary information: Supplementary data are available at Bioinformatics online.

Source: Bioinformatics - August 31, 2016 Category: Bioinformatics Authors: Wang, S., Ma, J., Xu, J. Tags: PROTEINS Source Type: research