Improving SIM-based Annotation Method of Protein Sequence Using Support Vector Machine

Jung-Ying Wang; Cheng-Kang Liu; Hahn-Ming Lee

doi:10.14864/softscis.2006.0.2266.0

SCIS & ISIS 2006

Session ID : SU-E2-3

DOI https://doi.org/10.14864/softscis.2006.0.2266.0

Conference information

Host: Japan SOciety for Fuzzy Theory and intelligent informatics

Co-host: The Korea Fuzzy Logic and Intelligent Systems Society, IEEE Computational Intelligence Society, The International Fuzzy Systems Association, 21th Century COE Program "Creation of Agent-Based Social Systems Sciences"

SU-E2 Knowledge Extraction and Data Mining (2)

Improving SIM-based Annotation Method of Protein Sequence Using Support Vector Machine

*Jung-Ying Wang, Cheng-Kang Liu, Hahn-Ming Lee

Author information

Keywords: protein annotation, support vector machine, sequence similarity

CONFERENCE PROCEEDINGS FREE ACCESS

Details

Abstract

In this paper, we present a protein sequence annotation system, named as MAPS (Multiple Annotation for Protein Sequences), which provides a mechanism to extract multiple annotations from various types of biological data including the SwissProt keywords, InterPro signatures and GO terms. Meanwhile, MAPS can automatically eliminate the error annotations by a pre-trained SVM classifier. It assigns an annotation to the input protein sequence by considering all hit proteins with this annotation entirely, not only single hit protein. The experimental results show that the error annotations can be eliminated effectively and keep high accuracy on different types of annotations.

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