Protein Remote Homology Detection by Combining Profile-based Protein Representation with Local Alignment Kernel
Bin Liu1,2,3, Xiaolong Wang1,2, Ruifeng Xu1,2
, and Buzhou Tang1,4
1.School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
2.Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
3.Shanghai Key Laboratory of Intelligent Information Processing, Shanghai, China
4.School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
2.Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, China
3.Shanghai Key Laboratory of Intelligent Information Processing, Shanghai, China
4.School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
Abstract—Protein remote homology detection has attracted a great deal of interest as it is one of the most important problems in bioinformatics. Profile-based methods recently achieve the state-of-the-art performance. A key step to improve the performance of these methods is to find a suitable approach to use the evolutionary information in the profiles. In this study, we propose the profile-based protein representation to extract the evolutionary information from frequency profiles. In this approach, the frequency profiles calculated from the multiple sequence alignments outputted by PSI-BLAST are converted into several profile-based proteins and then the local alignment kernel (LA) is combined with these profile-based proteins for the prediction. Our experiments on a well-known benchmark show that the proposed approach can significantly improve the predictive performance.
Index Terms—Protein remote homology, Support Vector Machine, profile-based proteins
Cite: Bin Liu, Xiaolong Wang, Ruifeng Xu, and Buzhou Tang, "Protein Remote Homology Detection by Combining Profile-based Protein Representation with Local Alignment Kernel", Journal of Medical and Bioengineering, Vol. 3, No. 1, pp. 17-22, March 2014. Doi: 10.12720/jomb.3.1.17-22
Index Terms—Protein remote homology, Support Vector Machine, profile-based proteins
Cite: Bin Liu, Xiaolong Wang, Ruifeng Xu, and Buzhou Tang, "Protein Remote Homology Detection by Combining Profile-based Protein Representation with Local Alignment Kernel", Journal of Medical and Bioengineering, Vol. 3, No. 1, pp. 17-22, March 2014. Doi: 10.12720/jomb.3.1.17-22
Array