Není k dispozici

Kód výsledku v 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>

Výsledek na webu

—

DOI - Digital Object Identifier

—

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

—

Projekt

—

Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2004

Kód důvěrnosti údajů

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

Název periodika

Journal of Biomedical Informatics

ISSN

1532-0464

e-ISSN

—

Svazek periodika

37

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

16

Strana od-do

269-284

Kód UT WoS článku

—

EID výsledku v databázi Scopus

—