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Integrating subcellular location for improving machine learning models of remote homology detection in eukaryotic organisms

Paper ID Volume ID Publish Year Pages File Format Full-Text
15497 1417 2007 5 PDF Available
Title
Integrating subcellular location for improving machine learning models of remote homology detection in eukaryotic organisms
Abstract

A significant challenge in homology detection is to identify sequences that share a common evolutionary ancestor, despite significant primary sequence divergence. Remote homologs will often have less than 30% sequence identity, yet still retain common structural and functional properties. We demonstrate a novel method for identifying remote homologs using a support vector machine (SVM) classifier trained by fusing sequence similarity scores and subcellular location prediction. SVMs have been shown to perform well in a variety of applications where binary classification of data is the goal. At the same time, data fusion methods have been shown to be highly effective in enhancing discriminative power of data. Combining these two approaches in the application SVM-SimLoc resulted in identification of significantly more remote homologs (p-value < 0.006) than using either sequence similarity or subcellular location independently.

Keywords
Remote homology detection; Support vector machine; Kernel integration; Subcellular localization
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Integrating subcellular location for improving machine learning models of remote homology detection in eukaryotic organisms
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Publisher
Database: Elsevier - ScienceDirect
Journal: Computational Biology and Chemistry - Volume 31, Issue 2, April 2007, Pages 138–142
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
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Full-text PDF Download
Online Support
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