Pathway-based multi-class classification of lung cancer

Conference proceedings article

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Publication Details

Author list: Engchuan W., Chan J.H.

Publisher: Springer

Publication year: 2012

Volume number: 7667 LNCS

Issue number: PART 5

Start page: 697

End page: 702

Number of pages: 6

ISBN: 9783642344992

ISSN: 0302-9743

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84869075510&doi=10.1007%2f978-3-642-34500-5_82&partnerID=40&md5=571eb6b2baf19dda102fcc99fb8e59cb

Languages: English-Great Britain (EN-GB)

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Abstract

The advances in high throughput microarray technology have enabled genome-wide expression analysis to identify diagnostic biomarkers of various disease states. In this work, muti-class classification of lung cancer data is developed based on our previous accurate and robust binary-class classification using pathway activity data. In particular, the pathway activity of each pathway was inferred using a Negatively Correlated Feature Set (NCFS) method based on curated pathway data from MSigDB, which combines pathway data of many public databases such as KEGG, PubMed, BioCarta, etc. The developed technique was tested on three independent datasets as well as a merged dataset. The results show that using a two-stage binary classification process on independent datasets provided the best performance. Nonetheless, the multi-class SVM technique also yielded acceptable results. ฉ 2012 Springer-Verlag.

Keywords

gene expression analysis, lung cancer, multi-class classification, SVM