Integrating rules and neural nets for carcinogenicity prediction

Giuseppina Gini, Marco Lorenzini, Emilio Benfenati, Raffaella Brambilla, Luca Malvè

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

One approach to deal with real complex systems is to use more techniques in order to combine their different strengths and overcome each other's weakness to generate hybrid solutions. In this project we pointed out the needs of an improved system in toxicology prediction. An architecture able to satisfy these needs has been developed. The main tools we integrated are rules and ANN. We defined chemical structures of fragments responsible for carcinogenicity according to human experts, developing a module able to recognize these fragments into a chemical. Furthermore, we developed an ANN, using molecular descriptors as inputs to predict carcinogenicity as a numerical value. Finally, we developed an automatic learning program to combine the results into a classifications of carcinogenicity to man.

Original languageEnglish
Title of host publicationAnnual Conference of the North American Fuzzy Information Processing Society - NAFIPS
EditorsM.H. Smith, W.A. Gruver, L.O. Hall
Pages3003-3008
Number of pages6
Volume5
Publication statusPublished - 2001
EventJoint 9th IFSA World Congress and 20th NAFIPS International Conference - Vancouver, BC, Canada
Duration: Jul 25 2001Jul 28 2001

Other

OtherJoint 9th IFSA World Congress and 20th NAFIPS International Conference
CountryCanada
CityVancouver, BC
Period7/25/017/28/01

ASJC Scopus subject areas

  • Computer Science(all)
  • Media Technology

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    Gini, G., Lorenzini, M., Benfenati, E., Brambilla, R., & Malvè, L. (2001). Integrating rules and neural nets for carcinogenicity prediction. In M. H. Smith, W. A. Gruver, & L. O. Hall (Eds.), Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS (Vol. 5, pp. 3003-3008)