Protein Fold Recognition and Remote Homology Detection Based on Profile-Level Building Blocks

Author

Lin, Lei ; Shen, Yi ; Liu, Bin ; Wang, Xiaolong

Author_Institution

Sch. of Comput. Sci. & Technol., Harbin Inst. of Technol., Harbin, China

fYear

2010

fDate

23-25 April 2010

Firstpage

1

Lastpage

5

Abstract

Protein remote homology detection and fold recognition are central problems in bioinformatics. In this paper, two kinds of profile-level building blocks of protein sequences, binary profiles and N-nary profiles, are presented, which contain the evolutionary information of the protein sequence frequency profile. The two building blocks are applied for protein remote homology and fold detection tasks. The latent semantic analysis (LSA) model is adopted to further improve the performance of our methods. Experiment results show that the methods based on profile-level building blocks give better results compared to related methods.

Keywords

bioinformatics; object detection; proteins; support vector machines; N-nary profiles; binary profiles; bioinformatics; latent semantic analysis; protein fold recognition; protein remote homology detection; protein sequence frequency profile; support vector machine; Bioinformatics; Computer science; Frequency; Heuristic algorithms; Hidden Markov models; Iterative algorithms; Protein engineering; Protein sequence; Support vector machines; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Engineering and Computer Science (ICBECS), 2010 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-5315-3

Type

conf

DOI

10.1109/ICBECS.2010.5462512

Filename

5462512