Title:

Combining Pairwise Sequence Similarity and Support Vector Machines for Remote Protein Homology Detection

Author(s):

Liao, Li
Noble, William Stafford

Date:

2001

Type:

Technical reports

Department:

Computer Science

Permanent URL:

http://hdl.handle.net/10022/AC:P:29266

Series:

Columbia University Computer Science Technical Reports

Part Number:

CUCS-012-01

Publisher:

Department of Computer Science, Columbia University

Publisher Location:

New York

Abstract:

One key element in understanding the molecular machinery of the cell is to understand the meaning, or function, of each protein encoded in the genome. A very successful means of inferring the function of a previously unannotated protein is via sequence similarity with one or more proteins whose functions are already known. Currently, one of the most powerful such homology detection methods is the SVM-Fisher method of Jaakkola, Diekhans and Haussler (ISMB 2000). This method combines a generative, profile hidden Markov model (HMM) with a discriminative classification algorithm known as a support vector machine (SVM). The current work presents an alternative method for SVM-based protein classification. The method, SVM-pairwise, uses a pairwise sequence similarity algorithm such as Smith-Waterman in place of the HMM in the SVM-Fisher method. The resulting algorithm, when tested on its ability to recognize previously unseen families from the SCOP database, yields significantly better remote protein homology detection than SVM-Fisher, profile HMMs and PSI-BLAST.

Subject(s):

Computer science

Item views:

146

Metadata:

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