Computational Methods to Predict Protein Functions from Protein-Protein Interaction Networks

Author(s): Bihai Zhao, Jianxin Wang*, Fang-Xiang Wu.

Journal Name: Current Protein & Peptide Science

Volume 18 , Issue 11 , 2017

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Abstract:

Predicting functions of proteins is a key issue in the post-genomic era. Some experimental methods have been designed to predict protein functions. However, these methods cannot accommodate the vast amount of sequence data due to their inherent difficulty and expense. To address these problems, a lot of computational methods have been proposed to predict the function of proteins. In this paper, we provide a comprehensive survey of the current techniques for computational prediction of protein functions. We begin with introducing the formal description of protein function prediction and evaluation of prediction methods. We then focus on the various approaches available in categories of supervised and unsupervised methods for predicting protein functions. Finally, we discuss challenges and future works in this field.

Keywords: Protein-protein interaction, protein function prediction, neural network, frequent pattern, support vector machine, heterogeneous data fusion, functional similarity.

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Article Details

VOLUME: 18
ISSUE: 11
Year: 2017
Page: [1120 - 1131]
Pages: 12
DOI: 10.2174/1389203718666170505121219
Price: $58

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