Name
Abstract | ||
|---|---|---|
Motivation: Protein remote homology detection is one of the fundamental problems in computational biology, aiming to find protein sequences in a database of known structures that are evolutionarily related to a given query protein. Some computational methods treat this problem as a ranking problem and achieve the state-of-the-art performance, such as PSI-BLAST, HHblits and ProtEmbed. This raises the possibility to combine these methods to improve the predictive performance. In this regard, we are to propose a new computational method called ProtDec-LTR for protein remote homology detection, which is able to combine various ranking methods in a supervised manner via using the Learning to Rank (LTR) algorithm derived from natural language processing. Results: Experimental results on a widely used benchmark dataset showed that ProtDec-LTR can achieve an ROC1 score of 0.8442 and an ROC50 score of 0.9023 outperforming all the individual predictors and some state-of-the-art methods. These results indicate that it is correct to treat protein remote homology detection as a ranking problem, and predictive performance improvement can be achieved by combining different ranking approaches in a supervised manner via using LTR. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1093/bioinformatics/btv413 | BIOINFORMATICS |
Field | DocType | Volume |
Learning to rank,Computer science,Homology (biology),Artificial intelligence,Bioinformatics,Machine learning | Journal | 31 |
Issue | ISSN | Citations |
21 | 1367-4803 | 17 |
PageRank | References | Authors |
0.63 | 23 | 6 |