Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.compbiolchem.2008.07.015
DC FieldValue
dc.titleSparse optimal scoring for multiclass cancer diagnosis and biomarker detection using microarray data
dc.contributor.authorLeng, C.
dc.date.accessioned2014-10-28T05:15:23Z
dc.date.available2014-10-28T05:15:23Z
dc.date.issued2008-12
dc.identifier.citationLeng, C. (2008-12). Sparse optimal scoring for multiclass cancer diagnosis and biomarker detection using microarray data. Computational Biology and Chemistry 32 (6) : 417-425. ScholarBank@NUS Repository. https://doi.org/10.1016/j.compbiolchem.2008.07.015
dc.identifier.issn14769271
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/105382
dc.description.abstractGene expression data sets hold the promise to provide cancer diagnosis on the molecular level. However, using all the gene profiles for diagnosis may be suboptimal. Detection of the molecular signatures not only reduces the number of genes needed for discrimination purposes, but may elucidate the roles they play in the biological processes. Therefore, a central part of diagnosis is to detect a small set of tumor biomarkers which can be used for accurate multiclass cancer classification. This task calls for effective multiclass classifiers with built-in biomarker selection mechanism. We propose the sparse optimal scoring (SOS) method for multiclass cancer characterization. SOS is a simple prototype classifier based on linear discriminant analysis, in which predictive biomarkers can be automatically determined together with accurate classification. Thus, SOS differentiates itself from many other commonly used classifiers, where gene preselection must be applied before classification. We obtain satisfactory performance while applying SOS to several public data sets. © 2007 Elsevier Ltd. All rights reserved.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.compbiolchem.2008.07.015
dc.sourceScopus
dc.subjectBiomarker detection
dc.subjectMicroarray data analysis
dc.subjectMulticlass classification
dc.typeArticle
dc.contributor.departmentSTATISTICS & APPLIED PROBABILITY
dc.description.doi10.1016/j.compbiolchem.2008.07.015
dc.description.sourcetitleComputational Biology and Chemistry
dc.description.volume32
dc.description.issue6
dc.description.page417-425
dc.identifier.isiut000261257500005
Appears in Collections:Staff Publications

Show simple item record
Files in This Item:
There are no files associated with this item.

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.