Eva Armengol, Enric Plaza (auth.), Werner Dubitzky, Francisco Azuaje (eds.)9781402028595, 1-4020-2859-8, 1402028652, 9781402028656
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.
Table of contents :
Lazy Learning for Predictive Toxicology based on a Chemical Ontology….Pages 1-18
QSAR Modeling of Mutagenicity on Non-Congeneric Sets of Organic Compounds….Pages 19-35
Characterizing Gene Expression Time Series using a Hidden Markov Model….Pages 37-50
Analysis of Large-Scale mRNA Expression Data Sets by Genetic Algorithms….Pages 51-66
A Data-Driven, Flexible Machine Learning Strategy for the Classification of Biomedical Data….Pages 67-85
Cooperative Metaheuristics for Exploring Proteomic Data….Pages 87-106
Integrating Gene Expression Data, Protein Interaction Data, and Ontology-Based Literature Searches….Pages 107-127
Ontologies in Bioinformatics and Systems Biology….Pages 129-145
Natural Language Processing and Systems Biology….Pages 147-173
Systems Level Modeling of Gene Regulatory Networks….Pages 175-195
Computational Neuroscience for Cognitive Brain Functions….Pages 197-215
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