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

ABSTRACT:

Motivation

Protein intrinsically disordered regions (IDRs) play an important role in many biological processes. Two key properties of IDRs are (i) the occurrence is proteome-wide and (ii) the ratio of disordered residues is about 6%, which makes it challenging to accurately predict IDRs. Most IDR prediction methods use sequence profile to improve accuracy, which prevents its application to proteome-wide prediction since it is time-consuming to generate sequence profiles. On the other hand, the methods without using sequence profile fare much worse than using sequence profile.

Method

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 integr

SUBMITTER: Wang S 

PROVIDER: S-EPMC5013916 | biostudies-literature | 2016 Sep

REPOSITORIES: biostudies-literature