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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