Paper Info
Title
A survey of current trends in computational predictions of protein-protein interactions.
Abstract
Proteomics become an important research area of interests in life science after the completion of the human genome project. This scientific is to study the characteristics of proteins at the large-scale data level, and then gain a holistic and comprehensive understanding of the process of disease occurrence and cell metabolism at the protein level. A key issue in proteomics is how to efficiently analyze the massive amounts of protein data produced by high-throughput technologies. Computational technologies with low-cost and short-cycle are becoming the preferred methods for solving some important problems in post-genome era, such as protein-protein interactions (PPIs). In this review, we focus on computational methods for PPIs detection and show recent advancements in this critical area from multiple aspects. First, we analyze in detail the several challenges for computational methods for predicting PPIs and summarize the available PPIs data sources. Second, we describe the state-of-the-art computational methods recently proposed on this topic. Finally, we discuss some important technologies that can promote the prediction of PPI and the development of computational proteomics.
Year
DOI
Venue
2020
10.1007/s11704-019-8232-z
Frontiers of Computer Science
Keywords
Field
DocType
proteomics, protein-protein interactions, protein feature extraction, computational proteomics
Data level,Protein–protein interaction,Protein level,Proteomics,Computer science,Cell metabolism,Artificial intelligence,Human genome,Computational biology,Machine learning
Journal
Volume
Issue
ISSN
14
4
2095-2228
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Yan-Bin Wang144.45
Zhuhong You274855.20
Liping Li317736.54
Zhan-Heng Chen425.76