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Machine learning and screening data

  • Gilles Bisson
Chapter

Abstract

In all living beings, to differing degrees and with the help of extremely varying mechanisms (genetic, chemical or cultural), one observes an aptitude for acquiring new behaviour through their interaction with the environment. The objective of machine learning is to study and put into effect such mechanisms using artificial systems: robots, computers etc.

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References

AND INTERNET SITES: [Accamba]: website of the Accamba project: http://accamba.imag.fr/

  1. ALPHONSE E., ROUVEIROL C. (2000) Lazy propositionalisation for Relational Learning. In Proc. of the 14th European Conference on Artificial Intelligence (ECAI-2000), IOS Press, Berlin: 256-260Google Scholar
  2. BAJORATH J. (2002) Integration of virtual and high-throughput screening. Nat. Rev. Drug Discov. 1: 882-894CrossRefGoogle Scholar
  3. BESALÚ E., GIRONÉS X., AMAT L., CARBÓ-DORCA R. (2002) Molecular quantum similarity and the fundamentals of QSAR. Acc. Chem. Res. 35: 289-295CrossRefGoogle Scholar
  4. [Codessa]: website giving a list of descriptors organised by type: http://www.codessa-pro.com
  5. COOK D.J., HOLDER L.B. (1994) Substructure Discovery Using Minimum Description Length and Background Knowledge. J. Artif. Intell. Res. 1: 231-255Google Scholar
  6. CORNUEJOLS A., MICLET L. (2002) Apprentissage Artificiel. Eyrolles, ParisGoogle Scholar
  7. DEHASPE L., DE RAEDT L. (1997) Mining association rules in multi-relational databases. In Proc. of ILP’97 workshop, Springer Verlag, Berlin-Heidelberg-New York: 125-132Google Scholar
  8. DESHPANDE M., KURAMOCHI M., KARIPYS G. (2003) Frequent Sub-Structure-Based Approaches for Classifying Chemical Compounds. Technical Report. In Proc. of IEEE Int. Conference on Data Mining (ICDM03) IEEE Computer Society Press, Melbourne, FlorideGoogle Scholar
  9. FINN P., MUGGLETON S.H., PAGE D., SRINIVASAN A. (1998) Pharmacophore discovery using the Inductive Logic Programming system PROGOL. Machine Learning 30: 241-271CrossRefGoogle Scholar
  10. FLACH P., LACHICHE N. (2005) Naive Bayesian Classification of Structured Data. Machine Learning 57: 233-269CrossRefGoogle Scholar
  11. FLOWER D.R. (1998) On the properties of bit string-based measures of chemical similarity. J. Chem. Inf. Comput. Sci. 38: 379-386Google Scholar
  12. FRÖHLICH H., WEGNER J., SIEKER F., ZELL R. (2005) Optimal Assignment Kernels for Attributed Molecular Graphs. In Proc. of Int. Conf. on Machine Learning (ICML): 225-232Google Scholar
  13. GÄRTNER T. (2003) A survey of kernels for structured data. ACM SIGKDD Explorations Newsletter 5(1): 49-58CrossRefGoogle Scholar
  14. GONZALEZ J., HOLDER L., COOK D. (2001) Application of graph based concept learning to the predictive toxicology domain. In PTC Workshop at the 5th PKDD, Université de FreiburgGoogle Scholar
  15. HELMA C., GOTTMANN E., KRAMER S. (2000) Knowledge Discovery and Data Mining in Toxicology. Stat. Methods Med. Res. 9: 329-358CrossRefGoogle Scholar
  16. HELMA C., KRAMER S. (2003a) A survey of the Predictive Toxicology Challenge 2000-2001. Bioinformatics 19: 1179-1182CrossRefGoogle Scholar
  17. HELMA C., KRAMER S., DE RAEDT L. (2003b) The Molecular Feature Miner MolFea. In Proc. of the Beilstein Workshop 2002, Beilstein Institut, Frankfurt am MainGoogle Scholar
  18. [Helma-PredTox]: website offering data and tools for the prediction of toxicological properties: http://www.predictive-toxicology.org/
  19. [HIV-Data-Set-1997]: website offering a public dataset of screening results the AIDS Screening Results, (May’ 97 Release): http://dtpws4.ncifcrf.gov/DOCS/AIDS/AIDS_DATA.HTML
  20. KING R.D., MARCHAND-GENESTE N., ALSBERG B. (2001) A quantum mechanics based representation of molecules for machine inference. Electronic Transactions on Artificial Intelligence 5: 127-142Google Scholar
  21. KING R.D., WHELAN K.E., JONES F.M., REISER P.G., BRYANT C.H, MUGGLETON S.H., KELL D.B., OLIVER S.G. (2004) Functional genomic hypothesis generation and experimentation by a robot scientist. Nature 427: 247-252CrossRefGoogle Scholar
  22. KRAMER S., LAVRAC N., FLACH P. (2001) Propositionalization approaches to relational data mining. In Relational Data Mining (DZEROSKI S., LAVRAC N. Eds) Springer Verlag, Berlin-Heidelberg-New YorkGoogle Scholar
  23. LANDWEHR N., PASSERINI A., RAEDT L. D., FRASCONI P. (2006) kFOIL: Learning Simple Relational Kernels. In Proc. of Twenty-First National Conference on Artificial Intelligence (AAAI-06), AAAI, BostonGoogle Scholar
  24. LIPINSKI C.A., LOMBARDO F., DOMINY, B.W., FEENEY P.J. (2001) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development setting. Adv. Drug Deliv. Rev. 46: 3-26CrossRefGoogle Scholar
  25. [Liu-Yu-2002]: Features selection for data mining: a survey: http://www.public.asu.edu/~huanliu/sur-fs02.ps
  26. LIU H., YU L. (2005) Toward Integrating Feature Selection Algorithms for Classification and Clustering. IEEE Trans. on Knowledge and Data Engineering 17: 1-12CrossRefGoogle Scholar
  27. MARCHAND-GENESTE N., WATSON K.A., ALSBERG B.K., KING R.D. (2002) A new approach to pharmacophore mapping and QSAR analysis using Inductive Logic Programming. Application to thermolysin inhibitors and glycogen phosphorylase b inhibitors. J. Med. Chem. 45: 399-409CrossRefGoogle Scholar
  28. MAYER D., MOTOC I., MARSHALL G. (1987) A unique geometry of the active site of angiotensin-converting enzyme consistent with structure-activites studies. J. Comput. Aided Mol. Des. 1: 3-16CrossRefGoogle Scholar
  29. MICHALSKI R.S. (1986) Understanding the nature of learning: Issues and research directions. In Machine Learning: An Artificial Intelligence Approach, Vol. II, Morgan Kaufmann, San Francisco, CA : 3-25Google Scholar
  30. MITCHELL T. (1997) Machine learning. Mc Graw Hill, New York.Google Scholar
  31. OKADA T. (2003) Characteristic substructures and properties in chemical carcinogens studied by the cascade model. Bioinformatics 19: 1208-1215CrossRefGoogle Scholar
  32. QUINLAN J.R. (1993) C4.5: Programs for Empirical Learning. Morgan Kaufmann, San Francisco, CAGoogle Scholar
  33. QUINLAN J.R. (1990) Learning logical definitions from relations. Machine Learning 5: 239-266Google Scholar
  34. RUSSELL S.J., NORVIG P. (2003) Artificial Intelligence: a modern approach. Prentice-Hall, Upper Saddle River, New JerseyGoogle Scholar
  35. SEBAG M., ROUVEIROL C. (2000) Resource-bounded Relational Reasoning: Induction and Deduction Through Stochastic Matching. Machine Learning 38: 41-62CrossRefGoogle Scholar
  36. SEBAG M., AZÉ J., LUCAS N. (2003) Impact Studies and Sensitivity Analysis in Medical Data Mining with ROC-based Genetic Learning. In Proc. of IEEE Int. Conference on Data Mining (ICDM03), IEEE Computer Society Press, Melbourne, Floride: 637-640CrossRefGoogle Scholar
  37. SEIFERT M., WOLF K.,VITT D. (2003) Virtual high-throughput in silico screening. Biosilico 1: 143-149CrossRefGoogle Scholar
  38. SRINIVASAN A., KING R.D., MUGGLETON S. (1999) The role of background knowledge: using a problem from chemistry to examine the performance of an ILP program. In Technical Report PRGTR -08-99, Oxford University Computing Laboratory, OxfordGoogle Scholar
  39. STERNBERG M.J.E., MUGGLETON S.H. (2003) Structure-activity relationships (SAR) and pharmacophore discovery using inductive logic programming (ILP). QSAR and Combinatorial Science 22: 527-532CrossRefGoogle Scholar
  40. TODESCHINI R., CONSONNI V. (2000) Handbook of Molecular Descriptors (MANNHOLD R., KUBINYI H., TIMMERMAN H. Eds.) Wiley-VCH, WeinheimCrossRefGoogle Scholar
  41. VAPNIK V. (1998) The Statistical Learning Theory. John Wiley, New YorkGoogle Scholar
  42. WEININGER D. (1988) SMILES: a chemical language and information system. 1. Introduction and Encoding Rules. J. Chem. Inf. Comput. Sci. 28: 31-36Google Scholar
  43. WITTEN I.H., EIBE F. (2005) Data Mining: Practical Machine Learning Tools and Techniques. 2nd Edition, Morgan Kaufmann, San Francisco, CAGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Gilles Bisson
    • 1
  1. 1.Grenoble Institute of Applied MathematicsTIMC IMAG Laboratory - Joseph Fourier UniversityGrenobleFrance

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