PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Benchmark Data Set for in Silico Prediction of Ames Mutagenicity
Katja Hansen, Sebastian Mika, Timon Schröter, Andreas Sutter, Antonius ter Laak, Thomas Steger-Hartmann, Nikolaus Heinrich and Klaus-Robert Müller
Journal of Chemical Information and Modelling Volume 49, Number 9, pp. 2077-2081, 2009.

Abstract

Up to now, publicly available data sets to build and evaluate Ames mutagenicity prediction tools have been very limited in terms of size and chemical space covered. In this report we describe a new unique public Ames mutagenicity data set comprising about 6500 nonconfidential compounds (available as SMILES strings and SDF) together with their biological activity. Three commercial tools (DEREK, MultiCASE, and an off-the-shelf Bayesian machine learner in Pipeline Pilot) are compared with four noncommercial machine learning implementations (Support Vector Machines, Random Forests, k-Nearest Neighbors, and Gaussian Processes) on the new benchmark data set.

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EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6915
Deposited By:Katja Hansen
Deposited On:15 April 2010