{"type":"ScholarlyArticle","title":"An assessment of submissions made to the Predictive Toxicology Evaluation Challenge","authors":[{"name":"Ashwin Srinivasan","slug":"ashwin-srinivasan","given":"Ashwin","family":"Srinivasan","affiliation":""},{"name":"Ross D. King","slug":"ross-d-king","given":"Ross D.","family":"King","affiliation":""},{"name":"Douglas W. Bristol","slug":"douglas-w-bristol","given":"Douglas W.","family":"Bristol","affiliation":""}],"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 Pr edictive 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 deve loped with expe rt collaboration; and (2) their ex planatory value for toxicology. Th e principal findings were: (a) using structural information available from a standard modelling package, layman-devised features, and outcomes of established biological tests, resu lt s from MLderived 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 n ew 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 substantiall y - to the PTE Challenge.","doi":null,"url":"https://ijcai.org/proceedings/1999/40","pdf":"https://ijcai.org/proceedings/1999/0040.pdf","markdown":"https://ijcai.org/proceedings/1999/40.md","bibtex":"https://ijcai.org/proceedings/1999/bibtex/40","volume":{"slug":"1999","title":"Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 1999","url":"https://ijcai.org/proceedings/1999/","year":1999,"location":"Stockholm, Sweden","dates":"","editors":"Thomas Dean","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Challenge Papers","track":"Challenge Papers","number":40,"pages":{"first":270,"last":274},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}