Pathway activity inferences with negatively correlated features for pancreatic cancer classification

Conference proceedings article

Authors/Editors

Strategic Research Themes

No matching items found.

Publication Details

Author list: Sootanan P., Prom-on S., Meechai A., Chan J.H.

Publisher: Hindawi

Publication year: 2009

ISBN: 9781424441341

ISSN: 0146-9428

eISSN: 1745-4557

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-74049139643&doi=10.1109%2fBMEI.2009.5305092&partnerID=40&md5=77cb55460489b6d532eaa1fcfa0529f6

Languages: English-Great Britain (EN-GB)

View on publisher site

Abstract

Pathway-based analysis has been extended to perform disease classification of expression profiles. In this study, gene sets of related pathways of pancreatic cancer from KEGG are used for microarray-based pancreatic cancer classification by using pathway activity inferences with negatively correlated features. Pearson's correlation coefficient (PC) has been determined to be a suitable distance-based feature selection method with negatively correlated features. The results from classification performance when using gene sets in related pathways of pancreatic cancer suggest that not all related pathways are relevant to this disease, and the Jak-STAT signaling pathways is the most significant pathway. Pathway activity metrics of the proposed method with negatively correlated features can increase discriminative score and classification performance in many gene sets of related pathway of pancreatic cancer when compared to the original method of Lee et al. [7]. ฉ2009 IEEE.

Keywords

Negatively correlated features, Pancreatic cancer classification, Pathway activity, Pathway-based microarray analysis, Pathway markers