Quality improvement of rule-based gene group descriptions using information about GO terms importance occurring in premises of determined rules

Marek Sikora; Aleksandra Gruca

International Journal of Applied Mathematics and Computer Science (2010)

  • Volume: 20, Issue: 3, page 555-570
  • ISSN: 1641-876X

Abstract

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In this paper we present a method for evaluating the importance of GO terms which compose multi-attribute rules. The rules are generated for the purpose of biological interpretation of gene groups. Each multi-attribute rule is a combination of GO terms and, based on relationships among them, one can obtain a functional description of gene groups. We present a method which allows evaluating the influence of a given GO term on the quality of a rule and the quality of a whole set of rules. For each GO term, we compute how big its influence on the quality of generated set of rules and therefore the quality of the obtained description is. Based on the computed quality of GO terms, we propose a new algorithm of rule induction in order to obtain a more synthetic and more accurate description of gene groups than the description obtained by initially determined rules. The obtained GO terms ranking and newly obtained rules provide additional information about the biological function of genes that compose the analyzed group of genes.

How to cite

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Marek Sikora, and Aleksandra Gruca. "Quality improvement of rule-based gene group descriptions using information about GO terms importance occurring in premises of determined rules." International Journal of Applied Mathematics and Computer Science 20.3 (2010): 555-570. <http://eudml.org/doc/208007>.

@article{MarekSikora2010,
abstract = {In this paper we present a method for evaluating the importance of GO terms which compose multi-attribute rules. The rules are generated for the purpose of biological interpretation of gene groups. Each multi-attribute rule is a combination of GO terms and, based on relationships among them, one can obtain a functional description of gene groups. We present a method which allows evaluating the influence of a given GO term on the quality of a rule and the quality of a whole set of rules. For each GO term, we compute how big its influence on the quality of generated set of rules and therefore the quality of the obtained description is. Based on the computed quality of GO terms, we propose a new algorithm of rule induction in order to obtain a more synthetic and more accurate description of gene groups than the description obtained by initially determined rules. The obtained GO terms ranking and newly obtained rules provide additional information about the biological function of genes that compose the analyzed group of genes.},
author = {Marek Sikora, Aleksandra Gruca},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {decision rules; importance of rules premises; measures of rules interestingness; gene ontology; descriptions of gene groups},
language = {eng},
number = {3},
pages = {555-570},
title = {Quality improvement of rule-based gene group descriptions using information about GO terms importance occurring in premises of determined rules},
url = {http://eudml.org/doc/208007},
volume = {20},
year = {2010},
}

TY - JOUR
AU - Marek Sikora
AU - Aleksandra Gruca
TI - Quality improvement of rule-based gene group descriptions using information about GO terms importance occurring in premises of determined rules
JO - International Journal of Applied Mathematics and Computer Science
PY - 2010
VL - 20
IS - 3
SP - 555
EP - 570
AB - In this paper we present a method for evaluating the importance of GO terms which compose multi-attribute rules. The rules are generated for the purpose of biological interpretation of gene groups. Each multi-attribute rule is a combination of GO terms and, based on relationships among them, one can obtain a functional description of gene groups. We present a method which allows evaluating the influence of a given GO term on the quality of a rule and the quality of a whole set of rules. For each GO term, we compute how big its influence on the quality of generated set of rules and therefore the quality of the obtained description is. Based on the computed quality of GO terms, we propose a new algorithm of rule induction in order to obtain a more synthetic and more accurate description of gene groups than the description obtained by initially determined rules. The obtained GO terms ranking and newly obtained rules provide additional information about the biological function of genes that compose the analyzed group of genes.
LA - eng
KW - decision rules; importance of rules premises; measures of rules interestingness; gene ontology; descriptions of gene groups
UR - http://eudml.org/doc/208007
ER -

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