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By Eva Armengol, Enric Plaza (auth.), Werner Dubitzky, Francisco Azuaje (eds.)

This booklet offers at the same time a layout blueprint, consumer advisor, examine schedule, and verbal exchange platform for present and destiny advancements in man made intelligence (AI) techniques 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 e-book is meant for scientists and different pros tasked with constructing and utilizing AI applied sciences within the context of existence sciences learn. As a consumer consultant, this quantity addresses the necessities of researchers to achieve a simple figuring out of key AI methodologies for all times sciences study. Its emphasis isn't really on an complex mathematical therapy of the offered AI methodologies. as an alternative, it goals at offering the clients with a transparent figuring out and useful knowledge of the tools. As a learn time table, the ebook is meant for laptop and existence technology scholars, lecturers, researchers, and bosses who are looking to comprehend the cutting-edge of the awarded methodologies and the components within which gaps in our wisdom call for additional study and improvement. Our objective was once to take care of the clarity and accessibility of a textbook through the chapters, instead of compiling a trifling reference guide. The booklet can be meant as a verbal exchange platform looking to bride the cultural and technological hole between key structures biology disciplines. To help this functionality, participants have followed a terminology and strategy that attract audiences from assorted backgrounds.

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Mixing a symbolic 16. G. Gini, M. Lorenzini, E. Benfenati, R. Brambilla, and L. Malve. and subsymbolic expert to improve carcinogenicity prediction of aromatic compounds. In Multiple classifier systems. 2th Intern. Workshop, pages 126–135, 2001. 17. J. Gonzalez, L. Holder, and D. Cook. Graph based concept learning. In AAAI, page 1072, 2000. 18. C. Helma, E. Gottmann, and S. Kramer. Knowledge discovery and data mining in toxicology. Statistical Methods in Medical Research, 9:329–358, 2000. 19. C.

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