Carcinogenesis Predictions Using Inductive Logic Programming View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

1997

AUTHORS

Ashwin Srinivasan , Ross D. King , Stephen H. Muggleton , Michael J. E. Sternberg

ABSTRACT

Obtaining accurate structural alerts for the causes of chemical cancers is a problem of great scientific and humanitarian value. This chapter builds on our earlier research that demonstrated the use of Inductive Logic Programming (ILP) for predictions for the related problem of mutagenic activity amongst nitroaromatic molecules. Here we are concerned with predicting carcinogenic activity in rodent bioassays using data from the U.S. National Toxicology Program conducted by the National Institute of Environmental Health Sciences. The 330 chemicals used here are significantly more diverse than the mutagenesis study, and form the basis for obtaining Structure-Activity Relationships (SARs) relating molecular structure to cancerous activity in rodents. We describe the use of the ILP system Progol to obtain SARs from this data. The rules obtained from Progol are comparable in accuracy to those from expert chemists, and more accurate than most state-of-the-art toxicity prediction methods. The rules can also be interpreted to give clues about the biological and chemical mechanisms of cancerogenesis, and make use of those learned by Progol for mutagenesis. Finally, we present details of, and predictions for, an ongoing international blind trial aimed specifically at comparing prediction methods. More... »

PAGES

243-260

References to SciGraph publications

Book

TITLE

Intelligent Data Analysis in Medicine and Pharmacology

ISBN

978-1-4613-7775-7
978-1-4615-6059-3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-1-4615-6059-3_14

DOI

http://dx.doi.org/10.1007/978-1-4615-6059-3_14

DIMENSIONS

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