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.
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Engchuan, W., Chan, J.H. (2012). Pathway-Based Multi-class Classification of Lung Cancer. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34500-5_82
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DOI: https://doi.org/10.1007/978-3-642-34500-5_82
Publisher Name: Springer, Berlin, Heidelberg
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