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A Survey on Microarray Gene Expression Cancer Diagnosis
Abstract
A cancer diagnosis by using the DNA microarray data faces many challenges the most serious one being the presence of thousands of genes and only several dozens of patient's samples. The classification of different tumor types is of great importance in cancer diagnosis and drug discovery. Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery)or for assigning tumors to known classes (class prediction). The recent advent of DNA microarray technique has made simultaneous monitoring of thousands of gene expressions possible. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.
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References
Kuramochi, Michihiro, and George Karypis. "Gene classification using expression profiles: a feasibility study." International Journal on Artificial Intelligence Tools 14, no. 04 (2005): 641-660.
Cho, Sung-Bae, and Hong-Hee Won. "Machine learning in DNA microarray analysis for cancer classification." In Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003-Volume 19, pp. 189-198. Australian Computer Society, Inc., 2003.
Wang, Yu, Igor V. Tetko, Mark A. Hall, Eibe Frank, Axel Facius, Klaus FX Mayer, and Hans W. Mewes. "Gene selection from microarray data for cancer classification—a machine learning approach." Computational biology and chemistry 29, no. 1 (2005): 37-46.
Guyon, Isabelle, Jason Weston, Stephen Barnhill, and Vladimir Vapnik. "Gene selection for cancer classification using support vector machines." Machine learning 46, no. 1-3 (2002): 389-422.
Ramaswamy, Sridhar, and Todd R. Golub. "DNA microarrays in clinical oncology." Journal of Clinical Oncology 20, no. 7 (2002): 1932-1941.
Mischel, Paul S., Timothy F. Cloughesy, and Stanley F. Nelson. "DNA-microarray analysis of brain cancer: molecular classification for therapy." Nature Reviews Neuroscience 5, no. 10 (2004): 782-792.
Shyamsundar, Radha, et al. "A DNA microarray survey of gene expression in normal human tissues." Genome biology 6.3 (2005): R22.
Ambroise, Christophe, and Geoffrey J. McLachlan. "Selection bias in gene extraction on the basis of microarray gene-expression data." Proceedings of the national academy of sciences 99, no. 10 (2002): 6562-6566.
Tseng, Vincent S., and Ching-Pin Kao. "Efficiently mining gene expression data via a novel parameterless clustering method." Computational Biology and Bioinformatics, IEEE/ACM Transactions on 2.4 (2005): 355-365.
Wang, Yu, Igor V. Tetko, Mark A. Hall, Eibe Frank, Axel Facius, Klaus FX Mayer, and Hans W. Mewes. "Gene selection from microarray data for cancer classification—a machine learning approach." Computational biology and chemistry 29, no. 1 (2005): 37-46.
Lee, Jaewook, and Daewon Lee. "Dynamic characterization of cluster structures for robust and inductive support vector clustering." Pattern Analysis and Machine Intelligence, IEEE Transactions on 28.11 (2006): 1869-1874.
Yang, Wen-Hui, Dao-Qing Dai, and Hong Yan. "Biclustering of microarray data based on singular value decomposition." Emerging Technologies in Knowledge Discovery and Data Mining. Springer Berlin Heidelberg, 2007. 194-205.
Reis-Filho, Jorge S., and Lajos Pusztai. "Gene expression profiling in breast cancer: classification, prognostication, and prediction." The Lancet 378, no. 9805 (2011): 1812-1823.
Mukherjee, S. N., Stephen J. Roberts, Peter Sykacek, and Sarah J. Gurr. "Gene ranking using bootstrapped P-values." ACM SIGKDD Explorations Newsletter 5, no. 2 (2003): 16-22.
Weigelt, Britta, Lajos Pusztai, Alan Ashworth, and Jorge S. Reis-Filho. "Challenges translating breast cancer gene signatures into the clinic." Nature Reviews Clinical Oncology 9, no. 1 (2011): 58-64.
Sharma, Alok, et al. "Strategy of finding optimal number of features on gene expression data." Electronics letters 47.8 (2011): 480-482.
Wan, Shibiao, Man-WaiMak, and Sun-Yuan Kung. "mGOASVM: Multi-label protein subcellular localization based on gene ontology and support vector machines." BMC bioinformatics 13.1 (2012): 290.
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