An assessment of submissions made to the Predictive Toxicology Evaluation Challenge

A. Srinivasan, R.D. King, and D.W. Bristol. In Proceedings of the 16th International Joint Conference on Artificial Intelligence. Morgan Kaufmann, Los Angeles, CA, 1999. More behind this link.

Abstract

Constructing ``good'' models for chemical carcinogenesis was identified in IJCAI-97 as providing a substantial challenge to ``knowledge discovery'' programs. Attention was drawn to a comparative exercise which called for predictions on the outcome of $30$ rodent carcinogenicity bioassays. This -- the Predictive Toxicology Evaluation (or PTE) Challenge -- was seen to provide AI programs with an opportunity to participate in an enterprise of scientific merit, and a yardstick for comparison against strong competition. Here we provide an assessment of the machine learning (ML) submissions made. Models submitted are assessed on: (1) their accuracy, in comparison to models developed with expert collaboration; and (2) their explanatory value for toxicology. The principal findings were: (a) using structural information available from a standard modelling package, layman-devised features, and outcomes of established biological tests, results from ML-derived models were at least as good as those with expert-derived techniques. This was surprising; (b) the combined use of structural and biological features by ML-derived models was unusual, and suggested new avenues for toxicology modelling. This was also unexpected; and (c) significant effort was required to interpret the output of even the most ``symbolic'' of ML-derived models. Much of this could have been alleviated with measures for converting the results into a more ``toxicology-friendly'' form. As it stands, their absence is sufficient to prevent a whole-hearted acceptance of these promising methods by toxicologists. This suggests that ML techniques have been able to respond -- not fully, but nevertheless substantially -- to the PTE Challenge.

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An assessment of submissions made to the Predictive Toxicology Evaluation Challenge

Abstract

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