Download Artificial Intelligence Methods and Tools for Systems by W. Dubitzky, Francisco Azuaje PDF

By W. Dubitzky, Francisco Azuaje

ISBN-10: 0387947256

ISBN-13: 9780387947259

This booklet offers concurrently a layout blueprint, person consultant, learn time table, and verbal exchange platform for present and destiny advancements in man made intelligence (AI) ways to platforms biology. It areas an emphasis at the molecular size of lifestyles phenomena and in a single bankruptcy on anatomical and sensible modeling of the brain.

As layout blueprint, the ebook is meant for scientists and different execs tasked with constructing and utilizing AI applied sciences within the context of lifestyles sciences learn. As a consumer advisor, this quantity addresses the necessities of researchers to realize a easy realizing of key AI methodologies for all times sciences study. Its emphasis isn't really on an complicated mathematical therapy of the provided AI methodologies. in its place, it goals at delivering the clients with a transparent figuring out and sensible knowledge of the equipment. As a study schedule, the ebook is meant for machine and existence technology scholars, academics, researchers, and bosses who are looking to comprehend the cutting-edge of the awarded methodologies and the parts within which gaps in our wisdom call for extra learn and improvement. Our objective was once to take care of the clarity and accessibility of a textbook in the course of the chapters, instead of compiling a trifling reference guide. The e-book can also be meant as a verbal exchange platform trying to bride the cultural and technological hole between key platforms biology disciplines. To aid this functionality, participants have followed a terminology and procedure that entice audiences from various backgrounds.

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De Reaedt and P. Flach, editors, ECML-2001. Freiburg. , number 2167 in Lecture Notes in Artificial Intelligence, pages 13–24. Springer, 2001. 2. E. Armengol and E. Plaza. Similarity assessment for relational cbr. In David W. Aha and Ian Watson, editors, CBR Research and Development. Proceedings of the ICCBR 2001. , number 2080 in Lecture Notes in Artificial Intelligence, pages 44–58. Springer-Verlag, 2001. 3. E. Armengol and E. Plaza. Relational case-based reasoning for carcinogenic activity prediction.

D. M. Hawkins. Predicting mutagenicity of congeneric and diverse sets of chemicals using computed molecular descriptors: A hierarchical approach. In R. Benigni, editor, Quantitative Structure-Activity Relationship (QSAR) Models of Mutagens and Carcinogens, pages 207–234, Boca Raton, FL, 2003. CRC Press. 6. C. R. T. D. Gute. Prediction of mutagenicity of aromatic and heteroaromatic amines from structure: A hierarchical qsar approach. J Chem Inf Comp Sci, 41:671–678, 2001. 7. R. Benigni and A. Giuliani.

R. Katrizky, editors, Predictive Toxicology of Chemicals: Experiences and Impacts of AI Tools, pages 36–39. AAAI Press, 1999. 29. A. Srinivasan, S. D. J. Sternberg. Mutagenesis: Ilp experiments in a non-determinate biological domain. In Proceedings of the Fourth Inductive Logic Programming Workshop, 1994. 30. S. H. D. J. Stenberg. The predictive toxicology evaluation challenge. In IJCAI, Nagoya, Japan, pages 4–9. Morgan Kaufman, 1997. 31. J. Weininger. Smiles a chemical language and information system.

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Artificial Intelligence Methods and Tools for Systems Biology by W. Dubitzky, Francisco Azuaje

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