Contents; Preface; Lazy Learning for Predictive Toxicology based on a Chemical Ontology, Eva Armengol and Enric Plaza; QSAR Modeling of Mutagenicity on Non-Congeneric Sets of Organic Compounds, Uko Maran and Sulev Sild; Characterizing Gene Expression Time Series using a Hidden Markov Model, Sally McClean, Bryan Scotney and Steve Robinson; Analysis of Large-Scale mRNA Expression Data Sets by Genetic Algorithms, Chia Huey Ooi and Patrick Tan; A Data-Driven, Flexible Machine Learning Strategy for the Classification of Biomedical Data, Rajmund L. Somorjai, Murray E. Alexander, Richard Baumgartner, Stephanie Booth, Christopher Bowman, Aleksander Demko, Brion Dolenko, Marina Mandelzweig, Aleksander E. Nikulin, Nicolino J. Pizzi, Erinija Pranckeviciene, Arthur R. Summers and Peter Zhilkin; Cooperative Metaheuristics for Exploring Proteomic Data, Robin Gras, David Hernandez, Patricia Hernandez, Nadine Zangger, Yoan Mescam, Julien Frey, Olivier Martin, Jacques Nicolas and Ron D. Appel; Integrating Gene Expression Data, Protein Interaction Data, and Ontology-Based Literature Searches, Panos Dafas, Alexander Kozlenkov, Alan Robinson and Michael Schroeder; iv Contents; Ontologies in Bioinformatics and Systems Biology, Patrick Lambrix; Natural Language Processing and Systems Biology, K. Bretonnel Cohen and Lawrence Hunter; Systems Level Modeling of Gene Regulatory Networks, Martin Stetter, Bernd Sch¿urmann and Math¿aus Dejori; Computational Neuroscience for Cognitive Brain Functions, Marco Loh, Miruna Szabo, Rita Almeida, Martin Stetter and Gustavo Deco; Index;
This book provides simultaneously a design blueprint, user guide, research agenda, and communication platform for current and future developments in artificial intelligence (AI) approaches to systems biology. It places an emphasis on the molecular dimension of life phenomena and in one chapter on anatomical and functional modeling of the brain.
As design blueprint, the book is intended for scientists and other professionals tasked with developing and using AI technologies in the context of life sciences research. As a user guide, this volume addresses the requirements of researchers to gain a basic understanding of key AI methodologies for life sciences research. Its emphasis is not on an intricate mathematical treatment of the presented AI methodologies. Instead, it aims at providing the users with a clear understanding and practical know-how of the methods. As a research agenda, the book is intended for computer and life science students, teachers, researchers, and managers who want to understand the state of the art of the presented methodologies and the areas in which gaps in our knowledge demand further research and development. Our aim was to maintain the readability and accessibility of a textbook throughout the chapters, rather than compiling a mere reference manual. The book is also intended as a communication platform seeking to bride the cultural and technological gap among key systems biology disciplines. To support this function, contributors have adopted a terminology and approach that appeal to audiences from different backgrounds.
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