Gene expression profile class prediction using linear Bayesian classifiers

dc.contributor.author	Asyali, M.H.
dc.date.accessioned	2021-01-25T20:52:07Z
dc.date.available	2021-01-25T20:52:07Z
dc.date.issued	2007
dc.identifier	10.1016/j.compbiomed.2007.04.001
dc.identifier.issn	00104825
dc.identifier.uri	https://www.scopus.com/inward/record.uri?eid=2-s2.0-35348882637&doi=10.1016%2fj.compbiomed.2007.04.001&partnerID=40&md5=cb8959feb925e56f7822171c80874e48
dc.identifier.uri	https://dspace.yasar.edu.tr/xmlui/handle/20.500.12742/10752
dc.description.abstract	Due to recent advances in DNA microarray technology, using gene expression profiles, diagnostic category of tissue samples can be predicted with high accuracy. In this study, we discuss shortcomings of some existing gene expression profile classification
dc.language.iso	English
dc.publisher	Computers in Biology and Medicine
dc.title	Gene expression profile class prediction using linear Bayesian classifiers
dc.type	Article
dc.relation.firstpage	1690
dc.relation.lastpage	1699
dc.relation.volume	37
dc.relation.issue	12
dc.description.affiliations	Department of Computer Engineering, Yasar University, Kazim Dirik Mah. 364 Sok. No: 5, Bornova, 35500 Izmir, Turkey

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