Show simple item record King, Ross Donald Toivonen, H. Srinivasan, A. Kramer, S. Helma, C. 2006-04-25T15:34:53Z 2006-04-25T15:34:53Z 2003
dc.identifier.citation King , R D , Toivonen , H , Srinivasan , A , Kramer , S & Helma , C 2003 , ' Statistical Evaluation of the Predictive Toxicology Challenge 2000-2001 ' Bioinformatics , vol 19 , no. 10 , pp. 1183-1193 . DOI: 10.1093/bioinformatics/btg130 en
dc.identifier.issn 1367-4803
dc.identifier.other PURE: 68407
dc.identifier.other PURE UUID: dcd63082-240d-43bb-9f3d-a54fb610354d
dc.identifier.other dspace: 2160/150
dc.description Toivonen, H., Srinivasan, A., King, R. D., Kramer, S. and Helma, C. (2003) Statistical Evaluation of the Predictive Toxicology Challenge 2000-2001. Bioinformatics 19: 1183-1193 en
dc.description.abstract Motivation: The development of in silico models to predict chemical carcinogenesis from molecular structure would help greatly to prevent environmentally caused cancers. The Predictive Toxicology Challenge (PTC) competition was organized to test the state-of-the-art in applying machine learning to form such predictive models. Results: Fourteen machine learning groups generated 111 models. The use of Receiver Operating Characteristic (ROC) space allowed the models to be uniformly compared regardless of the error cost function. We developed a statistical method to test if a model performs significantly better than random in ROC space. Using this test as criteria five models performed better than random guessing at a significance level p of 0.05 (not corrected for multiple testing). Statistically the best predictor was the Viniti model for female mice, with p value below 0.002. The toxicologically most interesting models were Leuven2 for male mice, and Kwansei for female rats. These models performed well in the statistical analysis and they are in the middle of ROC space, i.e. distant from extreme cost assumptions. These predictive models were also independently judged by domain experts to be among the three most interesting, and are believed to include a small but significant amount of empirically learned toxicological knowledge. Availability: PTC details and data can be found at: en
dc.format.extent 11 en
dc.language.iso eng
dc.relation.ispartof Bioinformatics en
dc.rights en
dc.title Statistical Evaluation of the Predictive Toxicology Challenge 2000-2001 en
dc.type /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Bioinformatics and Computational Biology Group en
dc.description.status Peer reviewed en

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