By W. Dubitzky, Francisco Azuaje
This e-book offers concurrently a layout blueprint, consumer consultant, examine schedule, and communique platform for present and destiny advancements in man made intelligence (AI) techniques to structures biology. It locations an emphasis at the molecular measurement of lifestyles phenomena and in a single bankruptcy on anatomical and practical modeling of the brain.
As layout blueprint, the ebook is meant for scientists and different pros tasked with constructing and utilizing AI applied sciences within the context of lifestyles sciences examine. As a consumer consultant, this quantity addresses the necessities of researchers to realize a simple realizing of key AI methodologies for all times sciences examine. Its emphasis isn't really on an problematic mathematical therapy of the awarded AI methodologies. in its place, it goals at offering the clients with a transparent knowing and functional information of the equipment. As a learn schedule, the ebook is meant for machine and existence technology scholars, academics, researchers, and bosses who are looking to comprehend the state-of-the-art of the offered methodologies and the components during which gaps in our wisdom call for additional learn and improvement. Our goal used to be to take care of the clarity and accessibility of a textbook during the chapters, instead of compiling an insignificant reference guide. The ebook is additionally meant as a conversation platform trying to bride the cultural and technological hole between key structures biology disciplines. To aid this functionality, participants have followed a terminology and process that attract audiences from diverse backgrounds.
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Grasso, and M. Bruschi. 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, 1999. 19. E. Goldberg. Genetic Algorithm in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, MA, 1989. 20. S. C. Jurs. Prediction of the normal boiling points of organic compounds from molecular structures with a computational neural network model. J Chem Inf Comp Sci, 39:974–983, 1999.
Gini. Comet: the approach of a project in evaluating toxicity. C. R. Katrizky, editors, Predictive Toxicology of Chemicals: Experiences and Impacts of AI Tools, pages 40–43. AAAI Press, 1999. 8. V. A. K. O. S. Pankratova. Toxicology analysis by means of simple jsm method. Bioinformatics, 19(10):1201–1207, 2003. 9. F. Boulicaut and B. Cremilleux. δ-strong classification rules for characterizing chemical carcinogens. , 2001. 10. W. T. Wachsman, and A. Greenwell. The niehs predictive toxicology evaluation project.
AAAI Press, 1999. 7. E. Benfenati, S. Pelagatti, P. Grasso, and G. Gini. Comet: the approach of a project in evaluating toxicity. C. R. Katrizky, editors, Predictive Toxicology of Chemicals: Experiences and Impacts of AI Tools, pages 40–43. AAAI Press, 1999. 8. V. A. K. O. S. Pankratova. Toxicology analysis by means of simple jsm method. Bioinformatics, 19(10):1201–1207, 2003. 9. F. Boulicaut and B. Cremilleux. δ-strong classification rules for characterizing chemical carcinogens. , 2001. 10. W.