Experiments on the Accuracy of Algorithms for Inferring the Structure of Genetic Regulatory Networks from Microarray Expression Levels
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
After reviewing theoretical reasons for doubting that machine learning methods can accurately infer gene regulatory networks from microarray data, we test 10 algorithms on simulated data from the sea urchin network, and on microarray data for yeast compared with recent experimental determinations of the regulatory network in the same yeast species. Our results agree with the theoretical arguments: most algorithms are at chance for determining the existence of a regulatory connection between gene pairs, and the algorithms that perform better than chance are nonetheless so errorprone as to be of little practical use in these applications.
|Keywords||No keywords specified (fix it)|
No categories specified
(categorize this paper)
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library||
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
David Danks, Clark Glymour & Peter Spirtes (2003). The Computational and Experimental Complexity of Gene Perturbations for Regulatory Network Search. In W. H. Hsu, R. Joehanes & C. D. Page (eds.), Proceedings of IJCAI-2003 workshop on learning graphical models for computational genomics.
Clark Glymour, Two Statistical Problems for Inference to Regulatory Structure From Associations of Gene Expression Measurements with Microarrays.
Tianjaio Chu, Two Statistical Problems for Inference to Regulatory Structure From Associations of Gene Expression Measurements with Microarrays.
J. Aracena & J. Demongeot (2004). Mathematical Methods for Inferring Regulatory Networks Interactions: Application to Genetic Regulation. Acta Biotheoretica 52 (4).
Clark Glymour, The Computational and Experimental Complexity of Gene Perturbations for Regulatory Network Search.
Ehud Lamm (2009). Conceptual and Methodological Biases in Network Models. Annals of the New York Academy of Sciences 1178:291-304.
Rosario M. Piro (2011). Are All Genes Regulatory Genes? Biology and Philosophy 26 (4):595-602.
Hedi Ben Amor, Fabien Corblin, Eric Fanchon, Adrien Elena, Laurent Trilling, Jacques Demongeot & Nicolas Glade (forthcoming). Formal Methods for Hopfield-Like Networks. Acta Biotheoretica.
Roger Sansom (2008). Countering Kauffman with Connectionism: Two Views of Gene Regulation and the Fundamental Nature of Ontogeny. British Journal for the Philosophy of Science 59 (2):169-200.
Christophe Malaterre (2009). Are Self-Organizing Biochemical Networks Emergent? In Maryvonne Gérin & Marie-Christine Maurel (eds.), Origins of Life: Self-Organization and/or Biological Evolution? EDP Sciences. 117--123.
Thomas Bartz-Beielstein (2008). How Experimental Algorithmics Can Benefit From Mayo's Extensions to Neyman–Pearson Theory of Testing. Synthese 163 (3):385 - 396.
Added to index2010-12-22
Total downloads3 ( #290,026 of 1,098,670 )
Recent downloads (6 months)2 ( #174,018 of 1,098,670 )
How can I increase my downloads?