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

• Published in: Current protein & peptide science Vol. 18; no. 11; p. 1120
• Main Authors: Zhao, Bihai, Wang, Jianxin, Wu, Fang-Xiang
• Format: Journal Article
• Language: English
• Published: United Arab Emirates 01.01.2017
• Subjects: Computational Biology - methods; Decision Trees; Humans; Machine Learning; Neural Networks (Computer); Protein Interaction Mapping; Proteins - chemistry; Proteins - physiology; Support Vector Machine; support vector machine; Protein-protein interaction; heterogeneous data fusion; protein function prediction; functional similarity; frequent pattern; neural network
• ISSN: 1875-5550
• DOI: 10.2174/1389203718666170505121219

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.