Induction of comprehensible models for gene expression datasets by subgroup discovery methodology
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F04%3A03106374" target="_blank" >RIV/68407700:21230/04:03106374 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—
Alternative languages
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
Classification
Type
D - Article in proceedings
CEP classification
JD - Use of computers, robotics and its application
OECD FORD branch
—
Result continuities
Project
—
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)
Others
Publication year
2004
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2nd EUNITE Workshop on Intelligent Technologies for GeneExpression Based Individualized Medicine
ISBN
—
ISSN
—
e-ISSN
—
Number of pages
16
Pages from-to
269-284
Publisher name
BioControl Jena GmbH
Place of publication
Jena
Event location
Jena
Event date
May 14, 2004
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—