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Comprehensible Models for Predicting Molecular Interaction with Heart-Regulating Genes
Univ Boras, Sch Business & Informat, Boras, Sweden.
Univ Boras, Sch Business & Informat, Boras, Sweden.
AstraZeneca R&D, Södertälje, Sweden.
University of Skövde, School of Humanities and Informatics. University of Skövde, The Informatics Research Centre.
2008 (English)In: Proceedings of the Seventh International Conference on Machine Learning and Applications, IEEE Computer Society, 2008, 559-564 p.Conference paper, Published paper (Refereed)
Abstract [en]

 When using machine learning for in silico modeling, the goal is normally to obtain highly accurate predictive models. Often, however, models should also bring insights into interesting relationships in the domain. It is then desirable that machine learning techniques have the ability to obtain small and transparent models, where the user can control the tradeoff between accuracy, comprehensibility and coverage. In this study, three different decision list algorithms are evaluated on a data set concerning the interaction of molecules with a human gene that regulates heart functioning (hERG). The results show that decision list algorithms can obtain predictive performance not far from the state-of-the-art method random forests, but also that algorithms focusing on accuracy alone may produce complex decision lists that are very hard to interpret. The experiments also show that by sacrificing accuracy only to a limited degree, comprehensibility (measured as both model size and classification complexity) can be improved remarkably.

Place, publisher, year, edition, pages
IEEE Computer Society, 2008. 559-564 p.
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-2575DOI: 10.1109/ICMLA.2008.130ISI: 000263205800082Scopus ID: 2-s2.0-60649087754ISBN: 13:978-0-7695-3495-4 (print)OAI: oai:DiVA.org:his-2575DiVA: diva2:137535
Available from: 2009-01-21 Created: 2009-01-21 Last updated: 2013-03-18

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CiteExportLink to record
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Citation style
  • apa
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