Induction of comprehensible models for gene expression datasets by subgroup discovery methodology.

Abstract:

:Finding disease markers (classifiers) from gene expression data by machine learning algorithms is characterized by a high risk of overfitting the data due the abundance of attributes (simultaneously measured gene expression values) and shortage of available examples (observations). To avoid this pitfall and achieve predictor robustness, state-of-the-art approaches construct complex classifiers that combine relatively weak contributions of up to thousands of genes (attributes) to classify a disease. The complexity of such classifiers limits their transparency and consequently the biological insights they can provide. The goal of this study is to apply to this domain the methodology of constructing simple yet robust logic-based classifiers amenable to direct expert interpretation. On two well-known, publicly available gene expression classification problems, the paper shows the feasibility of this approach, employing a recently developed subgroup discovery methodology. Some of the discovered classifiers allow for novel biological interpretations.

journal_name

J Biomed Inform

authors

Gamberger D,Lavrac N,Zelezný F,Tolar J

doi

10.1016/j.jbi.2004.07.007

keywords:

subject

Has Abstract

pub_date

2004-08-01 00:00:00

pages

269-84

issue

4

eissn

1532-0464

issn

1532-0480

pii

S1532-0464(04)00075-9

journal_volume

37

pub_type

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