Computational Intelligence in Bioinformatics

Herausgegeben:Kelemen, Arpad; Abraham, Ajith; Chen, Yuehui

• Broschiertes Buch

Produktbeschreibung

Bioinformatics involve the creation and advancement of algorithms using techniques including computational intelligence, applied mathematics and statistics, informatics, and biochemistry to solve biological problems usually on the molecular level. Major research efforts in the field include sequence analysis, gene finding, genome annotation, protein structure alignment analysis and prediction, prediction of gene expression, protein-protein docking/interactions, and the modeling of evolution.

This book deals with the application of computational intelligence in bioinformatics. Addressing the various issues of bioinformatics using different computational intelligence approaches is the novelty of this edited volume.

Produktdetails

• Studies in Computational Intelligence 94
• Verlag: Springer / Springer Berlin Heidelberg / Springer, Berlin
• Artikelnr. des Verlages: 978-3-642-09550-4
• Softcover reprint of hardcover 1st ed. 2008
• Seitenzahl: 344
• Erscheinungstermin: 17. November 2010
• Englisch
• Abmessung: 235mm x 155mm x 19mm
• Gewicht: 523g
• ISBN-13: 9783642095504
• ISBN-10: 364209550X
• Artikelnr.: 32020444

Autorenporträt

Dr. Ajith Abraham is Director of the Machine Intelligence Research (MIR) Labs, a global network of research laboratories with headquarters near Seattle, WA, USA. He is an author/co-author of more than 750 scientific publications. He is founding Chair of the International Conference of Computational Aspects of Social Networks (CASoN), Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing (since 2008), and a Distinguished Lecturer of the IEEE Computer Society representing Europe (since 2011).

Inhaltsangabe

Computational Intelligence Algorithms and DNA Microarrays.- Inferring Gene Regulatory Networks from Expression Data.- Belief Networks for Bioinformatics.- Swarm Intelligence Algorithms in Bioinformatics.- Time Course Gene Expression Classification with Time Lagged Recurrent Neural Network.- Tree-Based Algorithms for Protein Classification.- Covariance-Model-Based RNA Gene Finding: Using Dynamic Programming versus Evolutionary Computing.- Fuzzy Classification for Gene Expression Data Analysis.- Towards the Enhancement of Gene Selection Performance.- Saccharomyces pombe and Saccharomyces cerevisiae Gene Regulatory Network Inference Using the Fuzzy Logic Network.- Multivariate Regression Applied to Gene Expression Dynamics.- The Amine System Project: Systems Biology in Practice.- DNA Encoding Methods in the Field of DNA Computing.