title: Decision tree and ensemble learning algorithms with their applications in bioinformatics.
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.
authors: Department of Computer Science, East Stroudsburg University, East Stroudsburg, PA 18301, USA. dche@po-box.esu.edu
authors: Dongsheng
authors: D
authors: Che
authors: Qi
authors: Q
authors: Liu
authors: Khaled
authors: K
authors: Rasheed
authors: Xiuping
authors: X
authors: Tao
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D003663:Decision Trees; D005260:Female; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D023281:Genomics / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D013058:Mass Spectrometry / Q000706:statistics & numerical data; D009369:Neoplasms / Q000737:chemistry / Q000145:classification / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data; D012044:Regression Analysis; D012984:Software