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The book (Table of contents)
Preface
Paolo Frasconi and Ron Shamir
Statistical Learning and Kernel Methods in Bioinformatics
Bernhard Schölkopf, Isabelle Guyon and Jason Weston p.1
Neural Networks and the Prediction of Protein Structure
Rita Casadio, Emidio Capriotti, Mario Compiani, Piero Fariselli, Irene Jacoboni,
Pier Luigi Martelli, Ivan Rossi and Gianluca Tasco p. 22
Neural Networks Predict Protein Structure: Hype or Hit?
Burkhard Rost p. 34
New Machine Learning Methods for the Prediction of Protein Topologies
Pierre Baldi, Gianluca Pollastri, Paolo Frasconi and Alessandro Vullo p. 51
Protein Folding using Contact Maps and Contact Vectors
Michele Vendruscolo p. 75
Multiple Sequence Alignments Information in Structure and Function Prediction
Damien Devos, Enrique Merino, Florencio Pazos and Alfonso Valencia p. 83
Role Assignments: Bacterial Protein Families and Superfamilies
Claudio Donati and Antonello Covacci p. 95
Pattern Discovery and the Algorithmics of Surprise
Alberto Apostolico p. 111
Techniques for Comparison, Pattern Matching and Pattern Discovery: From Sequences to Protein Topology
David Gilbert, David Westhead and Juris Viksna p. 128
Computational Identification of Regulatory Sites in DNA Sequences
Mikhail S. Gelfand p. 148
Computer System “Gene Discovery” for Promoter Structure Analysis
Nikolay A. Kolchanov, Mikhail A. Pozdnyakov, Yury L. Orlov, Oleg V. Vishnevsky, Nikolay L. Podkolodny, Eugenii E. Vityaev and Boris Kovalerchuk p. 173
Inductive Databases for Bio- and Chemo-Informatics
Luc De Raedt and Stefan Kramer p. 193
Near Genome Wide Expression Screening of Tumor Suppressor Pathways using Model Cell Lines with Inducible Transcription Factors – Data Acquisition and Analysis
Heiko Müller and Myriam Alcalay p. 208
Describing In-vitro Cell Proliferation and Transformation with Cellular Automata
Roberto Serra and Marco Villani p. 224
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