Pathway-Based multi-class classification of lung cancer

Authors:
Worrawat Engchuan;Jonathan H. Chan
Affiliations:
Data and Knowledge Engineering Laboratory (D-Lab), School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand;Data and Knowledge Engineering Laboratory (D-Lab), School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand
Venue:
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part V
Year:
2012

Citing 2
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Multiclass cancer classification and biomarker discovery using GA-based algorithms

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Pathway-based microarray analysis for robust disease classification

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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.