Raehoon Jeong
ABSTRACT
With the advent of inexpensive microarray technology, biologists have become increasingly reliant on gene expression analysis for detecting disease states, including diagnosis of cancerous tissue [12]. While random forests and SVMs have proven to be popular methods for expression analysis, little work has been done to compare these methods with AdaBoost, a popular ensemble learning algorithm, across a wide array of cancer prediction tasks. Our work shows AdaBoost outperforms other approaches on binary predictions while random forests and SVMs are the best choice in multi-class predictions.
- L. Breiman. Random forests. Machine Learning, 45(1):5--32, 2001. Google Scholar
Digital Library
- R. Díaz-Uriarte and S. A. De Andres. Gene selection and classification of microarray data using random forest. BMC Bioinformatics, 7(1):3, 2006.Google Scholar
- Y. Freund and R. E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1):119--139, 1997. Google ScholarDigital Library
- T. S. Furey, N. Cristianini, N. Duffy, et al. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics, 16.10:906--914, 2000.Google ScholarCross Ref
- N. Iizuka, M. Oka, H. Yamada-Okabe, et al. Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection. The Lancet, 361(9361):923--929, 2003.Google ScholarCross Ref
- R. Kuner, T. Muley, M. Meister, et al. Global gene expression analysis reveals specific patterns of cell junctions in non-small cell lung cancer subtypes. Lung Cancer, 63(1):32--38, 2009.Google ScholarCross Ref
- A. Naderi, A. Teschendorff, N. Barbosa-Morais, et al. A gene-expression signature to predict survival in breast cancer across independent data sets. Oncogene, 26(10):1507--1516, 2007.Google ScholarCross Ref
- D. T. Ross, U. Scherf, M. B. Eisen, et al. Systematic variation in gene expression patterns in human cancer cell lines. Nature Genetics, 24:227--235, 2000.Google ScholarCross Ref
- A. Sanchez-Palencia, M. Gomez-Morales, J. A. Gomez-Capilla, et al. Gene expression profiling reveals novel biomarkers in nonsmall cell lung cancer. International Journal of Cancer, 129(2):355--364, 2011.Google Scholar
- A. Statnikov, C. F. Aliferis, I. Tsamardinos, et al. A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics, 21(5):631--643, 2005. Google ScholarDigital Library
- A. Statnikov, L. Wang, and C. F. Aliferis. A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification. BMC Bioinformatics, 9(1):319, 2008.Google Scholar
- A. C. Tan and D. Gilbert. Ensemble machine learning on gene expression data for cancer classification. Applied Bioinformatics, 2:S75--83, 2003.Google Scholar
- E. Tian, F. Zhan, R. Walker, et al. The role of the wnt-signaling antagonist dkk1 in the development of osteolytic lesions in multiple myeloma. New England Journal of Medicine, 349(26):2483--2494, 2003.Google ScholarCross Ref
- V. N. Vapnik. Statistical Learning Theory, volume 1. Wiley New York, 1998.Google ScholarDigital Library
Index Terms
- A comprehensive analysis of classification algorithms for cancer prediction from gene expression
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