Pathway activity transformation for multi-class classification of lung cancer datasets

Journal article


Authors/Editors


Strategic Research Themes

No matching items found.


Publication Details

Author listEngchuan W., Chan J.H.

PublisherElsevier

Publication year2015

JournalNeurocomputing (0925-2312)

Volume number165

Start page81

End page89

Number of pages9

ISSN0925-2312

eISSN1872-8286

URLhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84929953593&doi=10.1016%2fj.neucom.2014.08.096&partnerID=40&md5=537499f698ca9cec5889b41dbc6e4ef3

LanguagesEnglish-Great Britain (EN-GB)


View in Web of Science | View on publisher site | View citing articles in Web of Science


Abstract

Pathway-based microarray analysis has been found to be a powerful tool to study disease mechanisms and to identify biological markers of complex diseases like lung cancer. From previous studies, the use of pathway activity transformed from gene expression data has been shown to be more informative in disease classification. However, current works on a pathway activity transformation method are for binary-class classification. In this study, we propose a pathway activity transformation method for multi-class data termed Analysis-of-Variance-based Feature Set (AFS). The classification results of using pathway activity derived from our proposed method show high classification power in three-fold cross-validation and robustness in across dataset validation for all four lung cancer datasets used. ฉ 2015 Elsevier B.V.


Keywords

multilayer perceptronPathway activity transformation


Last updated on 2023-03-10 at 07:36