Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/84511
Full metadata record
DC FieldValueLanguage
dc.contributor.authorWang, Lipo.en
dc.date.accessioned2013-06-07T03:57:54Zen
dc.date.accessioned2019-12-06T15:46:18Z-
dc.date.available2013-06-07T03:57:54Zen
dc.date.available2019-12-06T15:46:18Z-
dc.date.copyright2012en
dc.date.issued2012en
dc.identifier.citationWang, L. (2012). Feature selection in bioinformatics. Proceedings of SPIE-Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X, 8401.en
dc.identifier.urihttps://hdl.handle.net/10356/84511-
dc.description.abstractIn bioinformatics, there are often a large number of input features. For example, there are millions of single nucleotide polymorphisms (SNPs) that are genetic variations which determine the dierence between any two unrelated individuals. In microarrays, thousands of genes can be proled in each test. It is important to nd out which input features (e.g., SNPs or genes) are useful in classication of a certain group of people or diagnosis of a given disease. In this paper, we investigate some powerful feature selection techniques and apply them to problems in bioinformatics. We are able to identify a very small number of input features sucient for tasks at hand and we demonstrate this with some real-world data.en
dc.language.isoenen
dc.rights© 2012 Society of Photo-Optical Instrumentation Engineers (SPIE). This paper was published in Proceedings of SPIE-Independent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering X and is made available as an electronic reprint (preprint) with permission of Society of Photo-Optical Instrumentation Engineers (SPIE). The paper can be found at the following official DOI: [http://dx.doi.org/10.1117/12.921417].  One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.en
dc.subjectDRNTU::Engineering::Bioengineeringen
dc.titleFeature selection in bioinformaticsen
dc.typeConference Paperen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.contributor.conferenceIndependent Component Analyses, Compressive Sampling, Wavelets, Neural Net, Biosystems, and Nanoengineering (10th : 2012 : Baltimore, USA)en
dc.identifier.doi10.1117/12.921417en
dc.description.versionPublished versionen
item.fulltextWith Fulltext-
item.grantfulltextopen-
Appears in Collections:EEE Conference Papers
Files in This Item:
File Description SizeFormat 
5. Feature Selection in Bioinformatics.pdf232.67 kBAdobe PDFThumbnail
View/Open

SCOPUSTM   
Citations

3
checked on Sep 1, 2020

WEB OF SCIENCETM
Citations

1
checked on Sep 26, 2020

Page view(s)

366
checked on Sep 30, 2020

Download(s)

259
checked on Sep 30, 2020

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.