Predicting Chemical Carcinogenesis Using Structural Information Only
- Claire J. KennedyAffiliated withDepartment of Computer Science, Merchant Venturers Building, University of Bristol
- , Christophe Giraud-CarrierAffiliated withDepartment of Computer Science, Merchant Venturers Building, University of Bristol
- , Douglas W. BristolAffiliated withNational Institute of Environmental Health Sciences
This paper reports on the application of the Strongly Typed Evolutionary Programming System (STEPS) to the PTE2 challenge, which consists of predicting the carcinogenic activity of chemical compounds from their molecular structure and the outcomes of a number of laboratory analyses. Most contestants so far have relied heavily on results of short term toxicity (STT) assays. Using both types of information made available, most models incorporate attributes that make them strongly dependent on STT results. Although such models may prove to be accurate and informative, the use of toxicological information requires time cost and in some cases substantial utilisation of laboratory animals. If toxicological information only makes explicit, properties implicit in the molecular structure of chemicals, then provided a sufficiently expressive representation language, accurate solutions may be obtained from the structural information only. Such solutions may offer more tangible insight into the mechanistic paths and features that govern chemical toxicity as well as prediction based on virtual chemistry for the universe of compounds.
- Predicting Chemical Carcinogenesis Using Structural Information Only
- Book Title
- Principles of Data Mining and Knowledge Discovery
- Book Subtitle
- Third European Conference, PKDD’99, Prague, Czech Republic, September 15-18, 1999. Proceedings
- pp 360-365
- Print ISBN
- Online ISBN
- Series Title
- Lecture Notes in Computer Science
- Series Volume
- Series ISSN
- Springer Berlin Heidelberg
- Copyright Holder
- Springer-Verlag Berlin Heidelberg
- Additional Links
- Industry Sectors
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- Editor Affiliations
- 6. Computer Science Department, UNC Charlotte, Charlotte, N.C. 28223 and Institute of Computer Science, Polish Academy of Sciences
- 7. Faculty of Informatics and Statistics, University of Economics, Prague
- Author Affiliations
- 8. Department of Computer Science, Merchant Venturers Building, University of Bristol, Bristol, BS8 1UB, U.K.
- 9. National Institute of Environmental Health Sciences, Box 12233, RTP, NC, 27709, U.S.A.
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