The use of expert systems for toxicology risk prediction

One approach to predicting the toxicology of novel compounds is to apply expert knowledge. The field of artificial intelligence has identified a number of ways of doing this, and some of these approaches are briefly described in this chapter. We also examine two expert systems—derek, which predicts a variety of types of toxicology, and star, which predicts carcinogenicity—in some detail. star reasons about carcinogenicity using a system of argumentation. We believe that argumentation systems have great potential in this area, and so discuss them at length.

[1]  A. Troelstra,et al.  Constructivism in Mathematics: An Introduction , 1988 .

[2]  Robert A. Pollak,et al.  Government Risk Regulation , 1996 .

[3]  1986 Part II Environmental Protection Agency Guidelines for Carcinogen Risk Assessment , 2022 .

[4]  Philip N. Judson,et al.  Using Absolute and Relative Reasoning in the Prediction of the Potential Metabolism of Xenobiotics. , 2003 .

[5]  D. R. Tennant,et al.  Food chemical risk assessment , 1997 .

[6]  Gregory F. Cooper,et al.  The Computational Complexity of Probabilistic Inference Using Bayesian Belief Networks , 1990, Artif. Intell..

[7]  John D. Graham,et al.  How Risks are Identified and Assessed , 1996 .

[8]  Edward H. Shortliffe,et al.  Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence) , 1984 .

[9]  Talbot Page,et al.  A Generic View of Toxic Chemicals and Similar Risks , 1978 .

[10]  Robert J. Hanisch,et al.  Data reduction expert assistant , 1991 .

[11]  J. Ross Quinlan,et al.  Generating Production Rules from Decision Trees , 1987, IJCAI.

[12]  Philip N. Judson Rule induction for systems predicting biological activity , 1994, J. Chem. Inf. Comput. Sci..

[13]  Ferenc Darvas,et al.  HazardExpert: An Expert System for Predicting Chemical Toxicity , 1992 .

[14]  E H Shorthffe,et al.  Computer-based medical consultations mycin , 1976 .

[15]  Edward H. Shortliffe,et al.  Production Rules as a Representation for a Knowledge-Based Consultation Program , 1977, Artif. Intell..

[16]  Peter Ayton,et al.  Bias in human judgement under uncertainty? , 1995, The Knowledge Engineering Review.

[17]  Eric Horvitz,et al.  A decision-theoretic approach to the display of information for time-critical decisions: The Vista project , 1993 .

[18]  Nils J. Nilsson,et al.  Probabilistic Logic * , 2022 .

[19]  H S Rosenkranz,et al.  International Commission for Protection Against Environmental Mutagens and Carcinogens. Approaches to SAR in carcinogenesis and mutagenesis. Prediction of carcinogenicity/mutagenicity using MULTI-CASE. , 1994, Mutation research.

[20]  D. P. Lovell,et al.  Quantitative risk assessment , 1980 .

[21]  John Fox,et al.  Will it happen? Can it happen? A new approach to formal risk analysis , 1999 .

[22]  C. L. Hamblin Translation to and from Polish Notation , 1962, Comput. J..

[23]  Philip N. Judson,et al.  QSAR and Expert Systems in the Prediction of Biological Activity , 1992 .

[24]  Simon Parsons,et al.  Argumentation and risk assessment , 2002 .

[25]  Edward H. Shortliffe,et al.  A model of inexact reasoning in medicine , 1990 .

[26]  J E Ridings,et al.  Computer prediction of possible toxic action from chemical structure: an update on the DEREK system. , 1996, Toxicology.

[27]  Sally Popkorn First Steps in Modal Logic , 1995 .

[28]  Bruce G. Buchanan,et al.  On generality and problem solving: a case study using the DENDRAL program , 1970 .

[29]  Suresh H. Moolgavkar,et al.  12 Stochastic models of carcinogenesis , 1991 .

[30]  Peter McBurney,et al.  Dialectical Argumentation for Reasoning about Chemical Carcinogenicity , 2001, Log. J. IGPL.

[31]  Bruce G. Buchanan,et al.  Heuristic DENDRAL - A program for generating explanatory hypotheses in organic chemistry. , 1968 .

[32]  M. D. Barratt,et al.  Validation and Subsequent Development of the Derek Skin Sensitization Rulebase by Analysis of the BgVV List of Contact Allergens , 1999, J. Chem. Inf. Comput. Sci..

[33]  David Heckerman,et al.  Decision-theoretic troubleshooting , 1995, CACM.

[34]  Randall Davis,et al.  An overview of production systems , 1975 .

[35]  J. Goguen An introduction to algebraic semiotics, with application to user interface design , 1999 .

[36]  Bart Verheij,et al.  Automated argument assistance for lawyers , 1999, ICAIL '99.

[37]  K. Enslein,et al.  Use of SAR in computer-assited prediction of carcinogenicity and mutagenicity of chemicals by the TOPKAT program , 1994 .

[38]  Foster J. Provost,et al.  A Survey of Methods for Scaling Up Inductive Algorithms , 1999, Data Mining and Knowledge Discovery.

[39]  Philip N. Judson,et al.  Representation of Chemical Structures in Knowledge‐Based Systems: The StAR System. , 1997 .

[40]  David Heckerman,et al.  Probabilistic similarity networks , 1991, Networks.

[41]  Michael I. Jordan,et al.  Probabilistic Networks and Expert Systems , 1999 .

[42]  Catherine Petito Boyce,et al.  Comparison of Approaches for Developing Distributions for Carcinogenic Slope Factors , 1998 .

[43]  L. Thiel,et al.  The Non-Existence of Finite Projective Planes of Order 10 , 1989, Canadian Journal of Mathematics.

[44]  D. Sanderson,et al.  Computer Prediction of Possible Toxic Action from Chemical Structure; The DEREK System , 1991, Human & experimental toxicology.

[45]  Marcello Pera,et al.  The discourses of science , 1994 .

[46]  Philip N. Judson,et al.  A Comprehensive Approach to Argumentation , 2003, J. Chem. Inf. Comput. Sci..

[47]  Douglas S. Bridges,et al.  Can Constructive Mathematics be Applied in Physics? , 1999, J. Philos. Log..

[48]  Lorenz R. Rhomberg,et al.  A survey of methods for chemical health risk assessment among federal regulatory agencies , 1997 .

[49]  S. Toulmin The uses of argument , 1960 .

[50]  John Fox,et al.  A LOGIC OF ARGUMENTATION FOR REASONING UNDER UNCERTAINTY , 1995, Comput. Intell..

[51]  K. Appel,et al.  Every Planar Map Is Four Colorable , 2019, Mathematical Solitaires & Games.

[52]  Ashwin Srinivasan,et al.  Theories for Mutagenicity: A Study in First-Order and Feature-Based Induction , 1996, Artif. Intell..

[53]  Michael Luby,et al.  Approximating Probabilistic Inference in Bayesian Belief Networks is NP-Hard , 1993, Artif. Intell..

[54]  David Heckerman,et al.  Probabilistic Interpretation for MYCIN's Certainty Factors , 1990, UAI.

[55]  John P. McDermott,et al.  R1: A Rule-Based Configurer of Computer Systems , 1982, Artif. Intell..

[56]  Peter McBurney,et al.  Representing Epistemic Uncertainty by Means of Dialectical Argumentation , 2001, Annals of Mathematics and Artificial Intelligence.

[57]  John Fox,et al.  An argumentation-based approach to risk assesment , 1993 .

[58]  John Fox,et al.  Qualitative risk assessment fulfils a need , 1998, Applications of Uncertainty Formalisms.

[59]  John Fox,et al.  Using New Reasoning Technology in Chemical Information Systems , 1996, J. Chem. Inf. Comput. Sci..

[60]  Marvin Minsky,et al.  A framework for representing knowledge , 1974 .

[61]  Joyce J. Kaufman Strategy for computer‐generated theoretical and quantum chemical prediction of toxicity and toxicology (and pharmacology in general) , 1981 .

[62]  Marvin Minsky,et al.  A framework for representing knowledge" in the psychology of computer vision , 1975 .

[63]  Daniel P. Miranker TREAT: A Better Match Algorithm for AI Production System Matching , 1987, AAAI.

[64]  Robert D. Combes,et al.  The use of artificial intelligence systems for predicting toxicity , 1995 .

[65]  Charles L. Forgy,et al.  Rete: a fast algorithm for the many pattern/many object pattern match problem , 1991 .

[66]  Eric Horvitz,et al.  The Inconsistent Use of Measures of Certainty in Artificial Intelligence Research , 1985, UAI.

[67]  Lise Getoor,et al.  Learning Probabilistic Relational Models , 1999, IJCAI.

[68]  Ivan Bratko,et al.  Applications of inductive logic programming , 1995, CACM.

[69]  John Fox,et al.  Arguing about beliefs and actions , 1998, Applications of Uncertainty Formalisms.

[70]  Luc De Raedt,et al.  Inductive Logic Programming: Theory and Methods , 1994, J. Log. Program..

[71]  John Fox,et al.  Arguments, Contradicitions and Practical Reasoning , 1992, ECAI.

[72]  J D Graham,et al.  Historical perspective on risk assessment in the federal government. , 1995, Toxicology.

[73]  H. Kaminaka Computer-assisted design of organic synthesis , 1986 .

[74]  Enrique F. Castillo,et al.  Expert Systems and Probabilistic Network Models , 1996, Monographs in Computer Science.

[75]  G Klopman,et al.  Predicting toxicity through a computer automated structure evaluation program. , 1985, Environmental health perspectives.

[76]  Allan Leck Jensen,et al.  MIDAS: An Influence Diagram for Management of Mildew in Winter Wheat , 1996, UAI.

[77]  Luc De Raedt,et al.  Probabilistic logic learning , 2003, SKDD.

[78]  R. Koff,et al.  Meta-analysis, decision analysis, and cost-effectiveness analysis. Methods for quantitative synthesis in medicine , 1995 .

[79]  Simon Parsons,et al.  Reasoning with imperfect information , 2002 .

[80]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[81]  Philip N. Judson,et al.  Using Argumentation for Absolute Reasoning about the Potential Toxicity of Chemicals. , 2003 .

[82]  Dale Jamieson,et al.  Scientific Uncertainty and the Political Process , 1996 .

[83]  John D. Graham,et al.  In Search of Safety: Chemicals and Cancer Risk , 1988 .

[84]  P N Judson,et al.  Knowledge-based expert systems for toxicity and metabolism prediction: DEREK, StAR and METEOR. , 1999, SAR and QSAR in environmental research.

[85]  N. Greene Computer Software for Risk Assessment , 1997, J. Chem. Inf. Comput. Sci..

[86]  Robert Combes,et al.  Risk assessment: alternatives to animal testing , 1997 .