Prediction of Protein Subcellular Localization: A Machine Learning Approach

Subcellular localization is a key functional characteristic of proteins. Optimally combining available information is one of the key challenges in today's knowledge-based subcellular localization prediction approaches. This study explores machine learning approaches for the prediction of protei...

Full description

Saved in:

Bibliographic Details
Main Author:	SHIM, Kyong Jin
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2010
Subjects:	Databases and Information Systems Numerical Analysis and Scientific Computing
Online Access:	https://ink.library.smu.edu.sg/sis_research/1526 https://ink.library.smu.edu.sg/context/sis_research/article/2525/viewcontent/10_014.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!
Institution:	Singapore Management University
Language:	English

id	sg-smu-ink.sis_research-2525
record_format	dspace
spelling	sg-smu-ink.sis_research-25252018-08-16T06:45:05Z Prediction of Protein Subcellular Localization: A Machine Learning Approach SHIM, Kyong Jin Subcellular localization is a key functional characteristic of proteins. Optimally combining available information is one of the key challenges in today's knowledge-based subcellular localization prediction approaches. This study explores machine learning approaches for the prediction of protein subcellular localization that use resources concerning Gene Ontology and secondary structures. Using the spectrum kernel for feature representation of amino acid sequences and secondary structures, we explore an SVM-based learning method that classifies six subcellular localization sites: endoplasmic reticulum, extracellular, Golgi, membrane, mitochondria, and nucleus. 2010-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/1526 https://ink.library.smu.edu.sg/context/sis_research/article/2525/viewcontent/10_014.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Numerical Analysis and Scientific Computing
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	Databases and Information Systems Numerical Analysis and Scientific Computing
spellingShingle	Databases and Information Systems Numerical Analysis and Scientific Computing SHIM, Kyong Jin Prediction of Protein Subcellular Localization: A Machine Learning Approach
description	Subcellular localization is a key functional characteristic of proteins. Optimally combining available information is one of the key challenges in today's knowledge-based subcellular localization prediction approaches. This study explores machine learning approaches for the prediction of protein subcellular localization that use resources concerning Gene Ontology and secondary structures. Using the spectrum kernel for feature representation of amino acid sequences and secondary structures, we explore an SVM-based learning method that classifies six subcellular localization sites: endoplasmic reticulum, extracellular, Golgi, membrane, mitochondria, and nucleus.
format	text
author	SHIM, Kyong Jin
author_facet	SHIM, Kyong Jin
author_sort	SHIM, Kyong Jin
title	Prediction of Protein Subcellular Localization: A Machine Learning Approach
title_short	Prediction of Protein Subcellular Localization: A Machine Learning Approach
title_full	Prediction of Protein Subcellular Localization: A Machine Learning Approach
title_fullStr	Prediction of Protein Subcellular Localization: A Machine Learning Approach
title_full_unstemmed	Prediction of Protein Subcellular Localization: A Machine Learning Approach
title_sort	prediction of protein subcellular localization: a machine learning approach
publisher	Institutional Knowledge at Singapore Management University
publishDate	2010
url	https://ink.library.smu.edu.sg/sis_research/1526 https://ink.library.smu.edu.sg/context/sis_research/article/2525/viewcontent/10_014.pdf
_version_	1770571228613967872

Prediction of Protein Subcellular Localization: A Machine Learning Approach

Similar Items