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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F04%3A03105726" target="_blank" >RIV/68407700:21230/04:03105726 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

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

Original language description

Finding disease markers (classifiers) from gene expression data by machine learning algorithms is characterized by an especially 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-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 insight 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, we show the feasibility of this approach, employing a recently developed subgroup discovery methodology.

Czech name

Není k dispozici

Czech description

Není k dispozici

Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

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Project

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Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2004

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Biomedical Informatics

ISSN

1532-0464

e-ISSN

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Volume of the periodical

37

Issue of the periodical within the volume

4

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

16

Pages from-to

269-284

UT code for WoS article

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EID of the result in the Scopus database

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