Supervised learning with decision tree-based methods in computational and systems biology.

Abstract:

:At the intersection between artificial intelligence and statistics, supervised learning allows algorithms to automatically build predictive models from just observations of a system. During the last twenty years, supervised learning has been a tool of choice to analyze the always increasing and complexifying data generated in the context of molecular biology, with successful applications in genome annotation, function prediction, or biomarker discovery. Among supervised learning methods, decision tree-based methods stand out as non parametric methods that have the unique feature of combining interpretability, efficiency, and, when used in ensembles of trees, excellent accuracy. The goal of this paper is to provide an accessible and comprehensive introduction to this class of methods. The first part of the review is devoted to an intuitive but complete description of decision tree-based methods and a discussion of their strengths and limitations with respect to other supervised learning methods. The second part of the review provides a survey of their applications in the context of computational and systems biology.

journal_name

Mol Biosyst

journal_title

Molecular bioSystems

authors

Geurts P,Irrthum A,Wehenkel L

doi

10.1039/b907946g

subject

Has Abstract

pub_date

2009-12-01 00:00:00

pages

1593-605

issue

12

eissn

1742-206X

issn

1742-2051

journal_volume

5

pub_type

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