Artificial Intelligence Methods and Tools for Systems Biology

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W. Dubitzky, Francisco Azuaje
Springer Science & Business Media, 29.09.2007 - 221 Seiten
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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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Inhalt

QSAR Modeling of Mutagenicity on NonCongeneric Sets of Organic
19
Characterizing Gene Expression Time Series using a Hidden Markov
37
Analysis of LargeScale mRNA Expression Data Sets by Genetic
51
A DataDriven Flexible Machine Learning Strategy for the Classification
67
Cooperative Metaheuristics for Exploring Proteomic Data
86
Robin Gras David Hernandez Patricia Hernandez Nadine Zangger Yoan
107
Patrick Lambrix
129
Natural Language Processing and Systems Biology
146
Systems Level Modeling of Gene Regulatory Networks
175
Computational Neuroscience for Cognitive Brain Functions
196
Marco Loh Miruna Szabo Rita Almeida Martin Stetter and Gustavo Deco 197
217
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