AppearsinasupplementalissueofBioinformatics,basedon.doc

AppearsinasupplementalissueofBioinformatics,basedon.doc

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AppearsinasupplementalissueofBioinformatics,basedon.doc

Appears in a supplemental issue of Bioinformatics, based on the papers presented at the Tenth International Conference on Intelligent Systems for Molecular Biology, Edmonton, Canada, 2002 (also appears in:Proceedings of ISMB-2002) Evaluating Machine Learning Approaches for Aiding Probe Selection for Gene-Expression Arrays Tobler, J.B. Department of Computer Science University of Wisconsin 1210 West Dayton Street Madison, WI 53706 Molla, M.N. * Department of Computer Science University of Wisconsin 1210 West Dayton Street Madison, WI 53706 Shavlik, J.W. Departments of Computer Science and Biostatistics Medical Informatics University of Wisconsin 1210 West Dayton Street Madison, WI 53706 Nuwaysir, E. NimbleGen Systems, Inc. One Science Ct. Madison, WI 53711 Green, R. NimbleGen Systems, Inc. One Science Ct. Madison, WI 53711 Keywords: Microarrays, probe selection, artificial neural networks, decision trees, na?ve Bayes Detailed Contact Information: Michael N. Molla Department of Computer Science University of Wisconsin 1210 West Dayton Street Madison, WI 53706 Phone: (608) 263-7622 Fax: (608) 262-9777 Email: molla@ * To whom correspondence should be addressed. Abstract Motivation: Microarrays are a fast and cost-effective method of performing thousands of DNA hybridization experiments simultaneously. DNA probes are typically used to measure the expression level of specific genes. Because probes greatly vary in the quality of their hybridizations, choosing good probes is a difficult task. If one could accurately choose probes that are likely to hybridize well, then fewer probes would be needed to represent each gene in a gene-expression microarray, and, hence, more genes could be placed on an array of a given physical size. Our goal is to empirically evaluate how successfully three standard machine-learning algorithms - na?ve Bayes, decision trees, and artificial neural networks - can be applied to the task of predicting good probes. Fortunately i

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