Abstract
Machine learning approaches have wide applications in bioinformatics, and decision tree is one of the successful approaches applied in this field. In this chapter, we briefly review decision tree and related ensemble algorithms and show the successful applications of such approaches on solving biological problems. We hope that by learning the algorithms of decision trees and ensemble classifiers, biologists can get the basic ideas of how machine learning algorithms work. On the other hand, by being exposed to the applications of decision trees and ensemble algorithms in bioinformatics, computer scientists can get better ideas of which bioinformatics topics they may work on in their future research directions. We aim to provide a platform to bridge the gap between biologists and computer scientists.
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References
Baldi, P. and Brunak, S. (2001) Bioinformatics: The Machine Learning Approach (Adaptive Computation and Machine Learning), Second Edition. MIT, Cambridge, MA
Bhaskar, H., Hoyle, D.C. and Singh, S. (2006) Machine learning in bioinformatics: A brief survey and recommendations for practitioners, Computers in Biology and Medicine, 36, 1104–1125
Breiman, L., Friedman, J., Stone, C. and Olshen, R.A. (1984) Classification and Regression Trees. Chapman & Hall/CRC, New York, NY
Brieman, L. (1996) Bagging predictors, Machine Learning, 24, 123–140
Brieman, L. (2001) Random forests, Machine Learning, 45, 5–32
Che, D., Zhao, J., Cai, L. and Xu, Y. (2007) Operon prediction in microbial genomes using decision tree approach. In Proceedings of CIBCB. Honolulu, 135–142
David, H.W. (1992) Stacked generalization, Neural Networks, 5, 241–259
Diaz-Uriarte, R. and Alvarez de Andres, S. (2006) Gene selection and classification of microarray data using random forest, BMC Bioinformatics, 7, 3
Freund, Y. and Schapire, R. (1995) A decision-theoretic generalization of on-line learning and an application to boosting. In Proceedings of the Second European Conference on Computational Learning Theory. Springer, Berlin, 23–37
Ge, G. and Wong, G.W. (2008) Classification of premalignant pancreatic cancer mass-spectrometry data using decision tree ensembles, BMC Bioinformatics, 9, 275
Larranaga, P., Calvo, B., Santana, R., Bielza, C., Galdiano, J., Inza, I., Lozano, J.A., Armananzas, R., Santafe, G., Perez, A. and Robles, V. (2006) Machine learning in bioinformatics, Briefings in Bioinformatics, 7, 86–112
Middendorf, M., Kundaje, A., Wiggins, C., Freund, Y. and Leslie, C. (2004) Predicting genetic regulatory response using classification, Bioinformatics, 20 Suppl 1, i232–240
Qu, Y., Adam, B.L., Yasui, Y., Ward, M.D., Cazares, L.H., Schellhammer, P.F., Feng, Z., Semmes, O.J. and Wright, G.L., Jr. (2002) Boosted decision tree analysis of surface-enhanced laser desorption/ionization mass spectral serum profiles discriminates prostate cancer from noncancer patients, Clinical Chemistry, 48, 1835–1843
Quinlan, J.R. (1986) Induction of decision trees, Machine Learning, 1, 81–106
Quinlan, J.R. (1993) C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, CA
Salzberg, S., Delcher, A.L., Fasman, K.H. and Henderson, J. (1998) A decision tree system for finding genes in DNA, Journal of Computational Biology, 5, 667–680
Statnikov, A., Wang, L. and Aliferis, C.F. (2008) A comprehensive comparison of random forests and support vector machines for microarray-based cancer classification, BMC Bioinformatics, 9, 319
Tan, A.C. and Gilbert, D. (2003) Ensemble machine learning on gene expression data for cancer classification, Applied Bioinformatics, 2, S75–83
Vlahou, A., Schorge, J.O., Gregory, B.W. and Coleman, R.L. (2003) Diagnosis of ovarian cancer using decision tree classification of mass spectral data, Journal of Biomedicine and Biotechnology, 2003, 308–314
Wu, B., Abbott, T., Fishman, D., McMurray, W., Mor, G., Stone, K., Ward, D., Williams, K. and Zhao, H. (2003) Comparison of statistical methods for classification of ovarian cancer using mass spectrometry data, Bioinformatics, 19, 1636–1643
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Che, D., Liu, Q., Rasheed, K., Tao, X. (2011). Decision Tree and Ensemble Learning Algorithms with Their Applications in Bioinformatics. In: Arabnia, H., Tran, QN. (eds) Software Tools and Algorithms for Biological Systems. Advances in Experimental Medicine and Biology, vol 696. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-7046-6_19
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DOI: https://doi.org/10.1007/978-1-4419-7046-6_19
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