Counter propagation artificial neural network categorical models for prediction of carcinogenicity for non-congeneric chemicals

References

• Price , N . 2008 . Hail Caesar . Chem. Ind. , 15 : 18 – 19 . Available at http://www.caesar-project.eu/index.php?page=results&section=results2
   Google Scholar
• Benigni , R and Giuliani , A . 2003 . Putting the predictive toxicology challenge into perspective: reflections on the results . Bioinformatics , 19 : 1194 – 1200 .
   PubMed Web of Science ®Google Scholar
• Richard , AM and Benigni , R . 2002 . AI and SAR approaches for predicting chemical carcinogenicity: survey and status report . SAR QSAR Environ. Res. , 13 : 1 – 19 .
   PubMed Web of Science ®Google Scholar
• Patlewicz , G , Rodford , R and Walker , JD . 2003 . Quantitative structure-activity relationships for predicting mutagenicity and carcinogenicity . Environ. Toxicol. Chem. , 22 : 1885 – 1893 .
   PubMed Web of Science ®Google Scholar
• Helguera , AM , Perez , MCA , Combes , RD and González , MP . 2005 . The prediction of carcinogenicity from molecular structure . Curr. Comput-Aided Drug Des. , 1 : 237 – 255 .
   Web of Science ®Google Scholar
• Morales Helguera , A , Cabrera Perez , MA , Perez González , M , Molina Ruiz , R and Gonzalez-Diaz , H . 2005 . A topological substructural approach applied to the computational prediction of rodent carcinogenicity . Bioorg Med. Chem. , 13 : 2477 – 2488 .
   Google Scholar
• Passerini , L . 2003 . “ QSARs for individual classis of chemical mutagens and carcinogens ” . In Quantitative Structure-Activity Relationship (QSARs). Models of mutagens and carcinogens , Edited by: Benigni , R . 81 – 123 . Boca Raton, FL : CRC Press .
   Google Scholar
• Benigni , R , Giuliani , A , Gruska , A and Franke , R . 2003 . “ QSARs for the mutagenicity and carcinogenicity of Aromatic Amines ” . In Quantitative Structure-Activity Relationship (QSARs). Models of mutagens and carcinogens , Edited by: Benigni , R . 125 – 144 . Boca Raton, FL : CRC Press .
   Google Scholar
• Gini , G , Lorenzini , M , Benfenati , E , Grasso , P and Bruschi , M . 1999 . Predictive carcinogenicity: a model for aromatic compounds, with nitrogen-containing substituents, based on molecular descriptors using an artificial neural network . J. Chem. Inf. Comput. Sci. , 39 : 1076 – 1080 .
   PubMedGoogle Scholar
• Klopman , G , Chakravarti , SK , Zhu , H , Ivanov , JM and Saiakhov , RD . 2004 . ESP: A method to predict toxicity and pharmacological properties of chemicals using multiple MCASE databases . J. Chem. Inf. Comput. Sci. , 44 : 704 – 715 .
   PubMedGoogle Scholar
• Klopman , G , Ivanov , J , Saiakhov , R and Chakravarti , S . 2005 . “ MC4PC–An artificial intelligence approach to the discovery of quantitative structure-toxic activity relationship ” . In Predictive Toxicology , Edited by: Helma , C . 423 – 457 . Boca Raton, FL : CRC Press .
   Google Scholar
• Matthews , EJ and Contrera , JF . 1998 . A new highly specific method for predicting the carcinogenic potential of pharmaceuticals in rodents using enhanced MCASEQSAR-ES software . Regul. Toxicol. Pharmacol. , 28 : 242 – 264 .
   PubMed Web of Science ®Google Scholar
• Woo , YT and Lai , DY . 2005 . “ OncoLogic: A mechanism-based expert system for predicting the carcinogenic potential of chemicals ” . In Predictive Toxicology , Edited by: Helma , C . 385 – 413 . Boca Raton, FL : CRC Press .
   Google Scholar
• Lagunin , AA , Dearden , JC , Filimonov , DA and Poroikov , VV . 2005 . Computer-aided rodent carcinogenicity prediction . Mutat. Res. , 586 : 138 – 146 .
   PubMed Web of Science ®Google Scholar
• Benfenati , E and Gini , G . 1997 . Computational predictive programs (expert systems) in toxicology . Toxicology , 119 : 213 – 225 .
   PubMed Web of Science ®Google Scholar
• Benigni , R and Richard , AM . 1998 . Quantitative structure-based modeling applied to characterization and prediction of chemical toxicity . Methods , 14 : 264 – 276 .
   PubMed Web of Science ®Google Scholar
• Richard , AM . 1998 . Structure-based methods for predicting mutagenicity and carcinogenicity: are we there yet? . Mutat. Res. , 400 : 493 – 507 .
   PubMed Web of Science ®Google Scholar
• Contrera , JF , Matthews , EJ and Benz , RD . 2003 . Prediction the carcinogenic potential of pharmaceuticals in rodents using molecular structural similarity and E-state indices . Regul. Toxicol. Pharmacol. , 38 : 243 – 259 .
   PubMed Web of Science ®Google Scholar
• Loew , GH , Poulsen , M , Kirkjian , E , Ferrell , J , Sudhindra , BS and Rebagliati , M . 1985 . Computer-assisted mechanistic structure-activity studies: application to diverse classes of chemical carcinogens . Environ. Health. Perspect. , 61 : 69 – 96 .
   Google Scholar
• Benfenati , E , Benigni , R , DeMarini , DM , Helma , C , Kirkland , D , Martin , TM , Mazzatorta , P , Ouedraogo-Arras , G , Richard , AM , Schilter , B , Schoonen , WGE , Snyder , RD and Yang , C . 2009 . Predictive models for carcinogenicity: frameworks, state-of-the-art, and perspectives . J. Environ. Sci. Health C , 27 : 57 – 90 .
   Web of Science ®Google Scholar
• Benigni , R and Bossa , C . 2008 . Predictivity of QSAR . J. Chem. Inf. Model. , 48 : 971 – 980 .
   PubMed Web of Science ®Google Scholar
• Benigni , R , Bossa , C , Netzeva , T and Worth , A . 2007 . Collection and Evaluation of (Q)SAR Models for Mutagenicity and Carcinogenicity , 1 – 118 . European Commission Directorate General Joint Research Centre 2007, EUR 22772EN, © European Communities .
   Google Scholar
• Villemin , D , Cherqaoui , D and Mesbah , A . 1994 . Predicting carcinogenicity of polycyclic aromatic hydrocarbons from back-propagation neural network . J. Chem. Inf. Comput. Sci. , 34 : 1288 – 1293 .
   Google Scholar
• Benigni , R and Richard , AM . 1996 . QSARS of mutagens and carcinogens: two case studies illustrating problems in the construction of models for non-congeneric chemicals . Mutat. Res. , 371 : 29 – 46 .
   PubMed Web of Science ®Google Scholar
• Vračko , M . 1997 . A Study of structure-carcinogenic potency relationship with artificial neural networks. The using of descriptors related to geometrical and electronic structures . J. Chem. Inf. Comput. Sci. , 37 : 1037 – 1043 .
   Google Scholar
• Haykin , S . 1994 . Neural Networks: a Comprehensive Foundation , New York : Macmillan College Publishing Company .
   Google Scholar
• Gasteiger , J and Zupan , J . 1993 . Neural networks in chemistry . Angew. Chem. Int. Ed. , 32 : 503 – 527 .
   Web of Science ®Google Scholar
• Winkler , DA and Madellena , DJ . 1995 . QSAR and neural networks in life sciences . Ser. Math. Biol. Med. , 5 : 126 – 163 .
   Google Scholar
• Devillers , J . 1996 . Neural Networks in QSAR and Drug Design , London : Academic Press .
   Google Scholar
• Schneider , G and Wrede , P . 1998 . Artificial neural networks for computer-based molecular design . Prog. Biophys. Mol. Biol. , 70 : 175 – 222 .
   PubMed Web of Science ®Google Scholar
• Zupan , J and Gasteiger , J . 1999 . Neural Networks in Chemistry and Drug Design , 2nd , Weinheim : Wiley-VCH Verlag GmbH .
   Google Scholar
• Schneider , G . 2000 . Neural networks are useful tools for drug design . Neural Networks , 13 : 15 – 16 .
   PubMed Web of Science ®Google Scholar
• Ichikawa , H . 2003 . Hierarchy neural networks as applied to pharmaceutical problems . Adv. Drug. Delivery. Rev. , 55 : 1119 – 1147 .
   PubMed Web of Science ®Google Scholar
• Kohonen , T . 2001 . Self-Organizing Maps , Berlin : Springer .
   Google Scholar
• Schneider , G and Wrede , P . 1998 . Artificial neural networks for computer-based molecular design . Prog. Biophys. Mol. Biol. , 70 : 175 – 222 .
   PubMed Web of Science ®Google Scholar
• Taskinen , J and Yliruusi , J . 2003 . Prediction of physicochemical properties based on neural network modeling . Adv. Drug. Delivery Rev. , 55 : 1163 – 1183 .
   PubMed Web of Science ®Google Scholar
• CAESAR project web page, 2009. Available at http://www.caesar-project.eu
   Google Scholar
• Distributed Structure-Searchable Toxicity (DSSTox) Public Database Network. Available at http://www.epa.gov/ncct/dsstox/sdf_cpdbas.html
   Google Scholar
• Lois Gold Carcinogenic Potency Database (CPDB). Available at http://potency.berkeley.edu/cpdb.html
   Google Scholar
• Combes , R , Grindon , C , Cronin , MT , Roberts , DW and Garrod , JF . 2008 . Integrated decision-tree testing strategies for mutagenicity and carcinogenicity with respect to the requirements of the EU REACH legislation . ATLA , 36 : 43 – 63 .
   Google Scholar
• Schüürmann , G , Kühne , R , Kleint , F , Ebert , RU , Rothenbacher , C and Herth , P . 1997 . “ A software system for automatic chemical property estimation from molecular structure ” . In Quantitative Structure-Activity Relationships in Environmental Sciences , Edited by: Chen , F and Schüürmann , G . Vol. VII , 93 – 114 . Pensacola, FL : SETAC Press .
   Google Scholar
• Schüürmann , G , Ebert , RU , Nendza , M , Dearden , JC , Paschke , A and Kühne , R . 2007 . “ Prediction of fate-related compound properties ” . In Risk Assessment of Chemicals. An Introduction , 2nd , Edited by: Leeuwen , van K and Vermeire , T . 375 – 426 . Dordrecht, NL : Springer Science .
   Google Scholar
• Todeschini , R and Consonni , V . 2000 . Handbook of Molecular Descriptors , New York : Willey-VCH .
   Google Scholar
• Tetko , I , Gasteiger , J , Todeschini , R , Mauri , A , Livingstone , D , Ertl , P , Palyulin , V , Radchenko , E , Zefirov , N , Makarenko , A , Tanchuk , V and Prokopenko , V . 2005 . Virtual computational chemistry laboratory–design and description . J. Comput-Aided Mol. Des. , 19 : 453 – 463 .
   PubMed Web of Science ®Google Scholar
• CODESSA PRO User's Manual, 2005. Available at http://www.codessa-pro.com/manuals/manual.html
   Google Scholar
• MDL-QSAR version 2.2, MDL Information Systems Inc., 14600 Catalina St., San Leonardo, CA 94577, USA, 2002–2004; software available at http://www.drugdiscoveryonline.com/storefronts/mdl.html
   Google Scholar
• DRAGON home page. Available at http://www.talete.mi.it/products/dragon_description.html
   Google Scholar
• Diudea , MV (ed.) . 2001 . QSPR/QSAR Studies by Molecular Descriptors , Huntington, New York : Nova Science Publishers .
   Google Scholar
• Randić , M and Razinger , M . 1997 . “ On characterization of 3D molecular structures ” . In From Chemical Topology to Three-Dimensional Geometry , Edited by: Balaban , AT . 159 – 236 . New York : Plenum Press .
   Google Scholar
• Langewisch , A , Choobineh , F and Deng-Yuan , H . 1996 . Selection procedures in linear models . J. Stat. Plan. Infer. , 54 : 271 – 277 .
   Google Scholar
• Walmsley , AD . 1997 . Improved variable selection procedure for multivariate linear regression . Anal. Chim. Acta , 354 : 225 – 232 .
   Google Scholar
• Kano , Y . 2002 . Variable selection for structural models . J. Stat. Plan. Infer. , 108 : 173 – 187 .
   Google Scholar
• Shih , YS and Tsai , HW . 2004 . Variable selection bias in regression trees with constant fits . Comput. Stat. Data. Anal. , 45 : 595 – 607 .
   Web of Science ®Google Scholar
• Lucić , B , Trinajstić , N , Sild , S , Karelson , M and Katritzky , AR . 1999 . A new efficient approach for variable selection based on multiregression: prediction of gas chromatographic retention times and response factors . J. Chem. Inf. Comput. Sci. , 39 : 610 – 621 .
   Google Scholar
• Waller , CL and Bradley , MP . 1999 . Development and validation of a novel variable selection technique with application to multidimensional quantitative structure-activity relationships studies . J. Chem. Comput. Sci. , 39 : 345 – 355 .
   Google Scholar
• Zheng , W and Tropsha , A . 2000 . Novel variable selection quantitative structure-property relationship approach based on the k-nearest-neighbor principle . J. Chem. Comput. Sci. , 40 : 185 – 194 .
   PubMedGoogle Scholar
• Tang , K and Li , T . 2003 . Comparison of different partial least-squares methods in quantitative structure-activity relationships . Anal. Chim. Acta , 476 : 85 – 92 .
   Google Scholar
• Xu , L and Zhang , WJ . 2001 . Comparison of different methods for variable selection . Anal. Chim. Acta , 446 : 475 – 481 .
   Web of Science ®Google Scholar
• Despagne , F and Massart , DL . 1998 . Variable selection for neural networks in multivariate calibration . Chemom. Intell. Lab. Syst. , 40 : 145 – 163 .
   Google Scholar
• Castellano , G and Fanelli , AM . 2000 . Variable selection using neural-network models . Neurocomputing , 31 : 1 – 13 .
   Google Scholar
• Ramadan , Z , Song , XH , Hopke , PK , Johnson , MJ and Scow , KM . 2001 . Variable selection in classification of environmental soil samples for partial least square and neural network models . Anal. Chim. Acta , 44 : 231 – 242 .
   Google Scholar
• Yasri , A and Hartsough , D . 2001 . Toward an optimal procedure for variable selection and QSAR model building . J. Chem. Comput. Sci. , 45 : 1218 – 1227 .
   Google Scholar
• Zupan , J , Novič , M and Ruisanchez , I . 1997 . Kohonen and counterpropagation artificial neural networks in analytical chemistry . Chemom. Intell. Lab. Syst. , 38 : 1 – 23 .
   Web of Science ®Google Scholar
• Mazzatorta , P , Vračko , M , Jezierska , A and Benfenati , E . 2003 . Modeling toxicity by using supervised Kohonen neural networks . J. Chem. Inf. Comput. Sci. , 43 : 485 – 492 .
   PubMedGoogle Scholar
• AnnToolbox for Windows, National Institute of Chemistry, Ljubljana, Slovenia; software available at http://www.ki.si/en/display-pages/equipment/?tx_ukki_pi1%5Buid%5D=318&cHash=e267f7b447
   Google Scholar
• Cooper , JA , Saracci , R and Cole , P . 1979 . Describing the validity of carcinogen screening test . Br. J. Cancer. , 39 : 87 – 89 .
   PubMed Web of Science ®Google Scholar
• Fawcett , T . 2006 . An introduction to ROC analysis . Pattern Recognit. Lett. , 27 : 861 – 874 .
   Web of Science ®Google Scholar
• Fan , J , Upadhye , S and Worster , A . 2006 . Understanding receiver operating characteristic (ROC) curves . Can. J. Emerg. Med. , 8 : 19 – 20 .
   Google Scholar
• Van Trees , HL . 1968 . Detection, Estimation, and Modulation Theory (Part I) , New York : Wiley .
   Google Scholar
• Egan , JP . 1975 . Signal Detection Theory and ROC Analysis , New York : Academic Press .
   Google Scholar
• Swets , JA . 1988 . Measuring the accuracy of diagnostic systems . Science , 240 : 1285 – 1293 .
   PubMed Web of Science ®Google Scholar
• Bradley , AP . 1997 . The use of the area under the ROC curve in the evaluation of machine learning algorithms . Pattern Recognit. , 30 : 1145 – 1159 .
   Web of Science ®Google Scholar
• Swets , JA . 1996 . “ Signal Detection Theory and Roc Analysis in Psychology and Diagnostics ” . In Collected Papers , Mahwah, NJ : Lawrence Erlbaum Associates .
   Google Scholar
• Provost , F and Fawcett , T . 1997 . “ Analysis and Visualization of Classifier Performance: Comparison under Imprecise Class and Cost Distributions ” . In Proceedings of the Third International Conference of Knowledge Discovery and Data Mining (KDD-97) , 43 – 48 . Newport Beach, CA : AAAI Press .
   Google Scholar