TY - GEN
T1 - Mixing a symbolic and a subsymbolic expert to improve carcinogenicity prediction of aromatic compounds
AU - Gini, Giuseppina
AU - Lorenzini, Marco
AU - Benfenati, Emilio
AU - Brambilla, Raffaella
AU - Malvé, Luca
PY - 2001
Y1 - 2001
N2 - One approach to deal with real complex systems is to use two or 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. After them we developed specialized rules to recognize these fragments into a given chemical and to assess their toxicity. In practice the rule-based expert associates a category to each fragment found, then a category to the molecule. Furthermore, we developed an ANN-based expert that uses molecular descriptors in input and predicts carcinogenicity as a numerical value. Finally we added a classifier program to combine the results obtained from the two previous experts into a single predictive class of carcinogenicity to man.
AB - One approach to deal with real complex systems is to use two or 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. After them we developed specialized rules to recognize these fragments into a given chemical and to assess their toxicity. In practice the rule-based expert associates a category to each fragment found, then a category to the molecule. Furthermore, we developed an ANN-based expert that uses molecular descriptors in input and predicts carcinogenicity as a numerical value. Finally we added a classifier program to combine the results obtained from the two previous experts into a single predictive class of carcinogenicity to man.
UR - http://www.scopus.com/inward/record.url?scp=62149089034&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=62149089034&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:62149089034
SN - 3540422846
SN - 9783540422846
VL - 2096
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 126
EP - 135
BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PB - Springer Verlag
T2 - 2nd International Workshop on Multiple Classifier Systems, MCS 2001
Y2 - 2 July 2001 through 4 July 2001
ER -