Protein-protein interaction: A supervised learning approach

Master's

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Bibliographic Details
Main Author:	XIAO JUAN
Other Authors:	COMPUTER SCIENCE
Format:	Theses and Dissertations
Language:	English
Published:	2010
Subjects:	Protein-Protein Interaction, Maximum Entropy Model, Feature-based
Online Access:	http://scholarbank.nus.edu.sg/handle/10635/15166
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Institution:	National University of Singapore
Language:	English

id	sg-nus-scholar.10635-15166
record_format	dspace
spelling	sg-nus-scholar.10635-151662017-10-21T09:56:24Z Protein-protein interaction: A supervised learning approach XIAO JUAN COMPUTER SCIENCE TAN CHEW LIM Protein-Protein Interaction, Maximum Entropy Model, Feature-based Master's MASTER OF SCIENCE 2010-04-08T10:50:42Z 2010-04-08T10:50:42Z 2006-02-23 Thesis XIAO JUAN (2006-02-23). Protein-protein interaction: A supervised learning approach. ScholarBank@NUS Repository. http://scholarbank.nus.edu.sg/handle/10635/15166 NOT_IN_WOS en
institution	National University of Singapore
building	NUS Library
country	Singapore
collection	ScholarBank@NUS
language	English
topic	Protein-Protein Interaction, Maximum Entropy Model, Feature-based
spellingShingle	Protein-Protein Interaction, Maximum Entropy Model, Feature-based XIAO JUAN Protein-protein interaction: A supervised learning approach
description	Master's
author2	COMPUTER SCIENCE
author_facet	COMPUTER SCIENCE XIAO JUAN
format	Theses and Dissertations
author	XIAO JUAN
author_sort	XIAO JUAN
title	Protein-protein interaction: A supervised learning approach
title_short	Protein-protein interaction: A supervised learning approach
title_full	Protein-protein interaction: A supervised learning approach
title_fullStr	Protein-protein interaction: A supervised learning approach
title_full_unstemmed	Protein-protein interaction: A supervised learning approach
title_sort	protein-protein interaction: a supervised learning approach
publishDate	2010
url	http://scholarbank.nus.edu.sg/handle/10635/15166
_version_	1681079149075103744

Protein-protein interaction: A supervised learning approach

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