Evaluation of a Bayesian model-based approach in GA studies

Gábor Hullám, Péter Antal, Csaba Szalai, András Falus
; Proceedings of the third International Workshop on Machine Learning in Systems Biology, PMLR 8:30-43, 2009.

Abstract

In a typical Genetic Association Study (GAS) several hundreds to millions of genomic variables are measured and tested for association with a given set of a phenotypic variables (e.g., a given disease state or a complete expression profile), with the aim of identifying the genetic background of complex, multifactorial diseases. These highly varying requirements resulted in a number of different statistical tools applying different approaches either bayesian or non-bayesian, model-based or conditional. In this paper we evaluate dedicated GAS tools and general purpose feature subset selection (FSS) tools including a Bayesian model-based tool \emphBMLA in a GAS context. In the evaluation we used an artificial data set generated from a reference model with 113 genotypic variables that was based on a real-world genotype data.

Cite this Paper


BibTeX
@InProceedings{pmlr-v8-hullam10a, title = {Evaluation of a Bayesian model-based approach in GA studies}, author = {Gábor Hullám and Péter Antal and Csaba Szalai and András Falus}, pages = {30--43}, year = {2009}, editor = {Sašo Džeroski and Pierre Guerts and Juho Rousu}, volume = {8}, series = {Proceedings of Machine Learning Research}, address = {Ljubljana, Slovenia}, month = {05--06 Sep}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v8/hullam10a/hullam10a.pdf}, url = {http://proceedings.mlr.press/v8/hullam10a.html}, abstract = {In a typical Genetic Association Study (GAS) several hundreds to millions of genomic variables are measured and tested for association with a given set of a phenotypic variables (e.g., a given disease state or a complete expression profile), with the aim of identifying the genetic background of complex, multifactorial diseases. These highly varying requirements resulted in a number of different statistical tools applying different approaches either bayesian or non-bayesian, model-based or conditional. In this paper we evaluate dedicated GAS tools and general purpose feature subset selection (FSS) tools including a Bayesian model-based tool \emphBMLA in a GAS context. In the evaluation we used an artificial data set generated from a reference model with 113 genotypic variables that was based on a real-world genotype data.} }
Endnote
%0 Conference Paper %T Evaluation of a Bayesian model-based approach in GA studies %A Gábor Hullám %A Péter Antal %A Csaba Szalai %A András Falus %B Proceedings of the third International Workshop on Machine Learning in Systems Biology %C Proceedings of Machine Learning Research %D 2009 %E Sašo Džeroski %E Pierre Guerts %E Juho Rousu %F pmlr-v8-hullam10a %I PMLR %J Proceedings of Machine Learning Research %P 30--43 %U http://proceedings.mlr.press %V 8 %W PMLR %X In a typical Genetic Association Study (GAS) several hundreds to millions of genomic variables are measured and tested for association with a given set of a phenotypic variables (e.g., a given disease state or a complete expression profile), with the aim of identifying the genetic background of complex, multifactorial diseases. These highly varying requirements resulted in a number of different statistical tools applying different approaches either bayesian or non-bayesian, model-based or conditional. In this paper we evaluate dedicated GAS tools and general purpose feature subset selection (FSS) tools including a Bayesian model-based tool \emphBMLA in a GAS context. In the evaluation we used an artificial data set generated from a reference model with 113 genotypic variables that was based on a real-world genotype data.
RIS
TY - CPAPER TI - Evaluation of a Bayesian model-based approach in GA studies AU - Gábor Hullám AU - Péter Antal AU - Csaba Szalai AU - András Falus BT - Proceedings of the third International Workshop on Machine Learning in Systems Biology PY - 2009/03/02 DA - 2009/03/02 ED - Sašo Džeroski ED - Pierre Guerts ED - Juho Rousu ID - pmlr-v8-hullam10a PB - PMLR SP - 30 DP - PMLR EP - 43 L1 - http://proceedings.mlr.press/v8/hullam10a/hullam10a.pdf UR - http://proceedings.mlr.press/v8/hullam10a.html AB - In a typical Genetic Association Study (GAS) several hundreds to millions of genomic variables are measured and tested for association with a given set of a phenotypic variables (e.g., a given disease state or a complete expression profile), with the aim of identifying the genetic background of complex, multifactorial diseases. These highly varying requirements resulted in a number of different statistical tools applying different approaches either bayesian or non-bayesian, model-based or conditional. In this paper we evaluate dedicated GAS tools and general purpose feature subset selection (FSS) tools including a Bayesian model-based tool \emphBMLA in a GAS context. In the evaluation we used an artificial data set generated from a reference model with 113 genotypic variables that was based on a real-world genotype data. ER -
APA
Hullám, G., Antal, P., Szalai, C. & Falus, A.. (2009). Evaluation of a Bayesian model-based approach in GA studies. Proceedings of the third International Workshop on Machine Learning in Systems Biology, in PMLR 8:30-43

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