Artificial Intelligence Methods and Tools for Systems Biology

TL;DR: This book presentsazy Learning for Predictive Toxicology based on a Chemical Ontology, a Data-Driven, Flexible Machine Learning Strategy for the Classification of Biomedical Data, and a Directory of Ontology-Based Literature Searches.

Abstract: 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 Schurmann and Mathaus Dejori Computational Neuroscience for Cognitive Brain Functions, Marco Loh, Miruna Szabo, Rita Almeida, Martin Stetter and Gustavo Deco Index