Evolutionary search for improved path diagramsShow others and affiliations
2007 (English)In: Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics: 5th European Conference, EvoBIO 2007, Valencia, Spain, April 11-13, 2007. Proceedings / [ed] Elena Marchiori, Jason H. Moore, Jagath C. Rajapakse, Springer Berlin/Heidelberg, 2007, p. 114-121Conference paper, Published paper (Refereed)
Abstract [en]
A path diagram relates observed, pairwise, variable correlations to a functional structure which describes the hypothesized causal relations between the variables. Here we combine path diagrams, heuristics and evolutionary search into a system which seeks to improve existing gene regulatory models. Our evaluation shows that once a correct model has been identified it receives a lower prediction error compared to incorrect models, indicating the overall feasibility of this approach. However, with smaller samples the observed correlations gradually become more misleading, and the evolutionary search increasingly converges on suboptimal models. Future work will incorporate publicly available sources of experimentally verified biological facts to computationally suggest model modifications which might improve the model’s fitness.
Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2007. p. 114-121
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 0302-9743, E-ISSN 1611-3349 ; 4447 LNCS
Keywords [en]
networks, expression
National Category
Bioinformatics (Computational Biology)
Research subject
Bioinformatics
Identifiers
URN: urn:nbn:se:his:diva-2124DOI: 10.1007/978-3-540-71783-6_11ISI: 000246102100011Scopus ID: 2-s2.0-38049047606ISBN: 978-3-540-71782-9 (print)ISBN: 978-3-540-71783-6 (electronic)OAI: oai:DiVA.org:his-2124DiVA, id: diva2:32400
Conference
5th European Conference, EvoBIO 2007, Valencia, Spain, April 11-13, 2007
2008-06-032008-06-032021-11-19Bibliographically approved