Paper Info
Title
ProtFold-DFG: protein fold recognition by combining Directed Fusion Graph and PageRank algorithm
Abstract
As one of the most important tasks in protein structure prediction, protein fold recognition has attracted more and more attention. In this regard, some computational predictors have been proposed with the development of machine learning and artificial intelligence techniques. However, these existing computational methods are still suffering from some disadvantages. In this regard, we propose a new network-based predictor called ProtFold-DFG for protein fold recognition. We propose the Directed Fusion Graph (DFG) to fuse the ranking lists generated by different methods, which employs the transitive closure to incorporate more relationships among proteins and uses the KL divergence to calculate the relationship between two proteins so as to improve its generalization ability. Finally, the PageRank algorithm is performed on the DFG to accurately recognize the protein folds by considering the global interactions among proteins in the DFG. Tested on a widely used and rigorous benchmark data set, LINDAHL dataset, experimental results show that the ProtFold-DFG outperforms the other 35 competing methods, indicating that ProtFold-DFG will be a useful method for protein fold recognition.
Year
DOI
Venue
2021
10.1093/bib/bbaa192
BRIEFINGS IN BIOINFORMATICS
Keywords
DocType
Volume
protein fold recognition, Directed Fusion Graph, PageRank, KL divergence, transitive closure
Journal
22
Issue
ISSN
Citations 
3
1467-5463
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Jiangyi Shao100.68
Bin Liu241933.30