Data Mining Techniques for the Life Sciences

• Databases.- Nucleic Acid Sequence and Structure Databases.- Genomic Databases and Resources at the National Center for Biotechnology Information.- Protein Sequence Databases.- Protein Structure Databases.- Protein Domain Architectures.- Thermodynamic Database for Proteins: Features and Applications.- Enzyme Databases.- Biomolecular Pathway Databases.- Databases of Protein–Protein Interactions and Complexes.- Data Mining Techniques.- Proximity Measures for Cluster Analysis.- Clustering Criteria and Algorithms.- Neural Networks.- A User’s Guide to Support Vector Machines.- Hidden Markov Models in Biology.- Database Annotations and Predictions.- Integrated Tools for Biomolecular Sequence-Based Function Prediction as Exemplified by the ANNOTATOR Software Environment.- Computational Methods for Ab Initio and Comparative Gene Finding.- Sequence and Structure Analysis of Noncoding RNAs.- Conformational Disorder.- Protein Secondary Structure Prediction.- Analysis and Prediction of Protein Quaternary Structure.- Prediction of Posttranslational Modification of Proteins from Their Amino Acid Sequence.- Protein Crystallizability.

From the reviews:“The book consists of three parts with 22 chapters prepared by well-known experts from many countries. … book will be useful for students and researchers, such as biochemists, molecular biologists, and biotechnologists, who wish to get a condensed introduction to the world of biological databases and their applications related to various aspects of life science.” (G. Ya. Wiederschain, Biochemistry, Vol. 76 (4), 2011)“Provides a comprehensive overview and reference for molecular biologists and bioinformaticians as to the goals and scope of each database in each category. … The chapters are well written and provide a good introduction to the addressed topics … . Each chapter is an interesting and informative read in itself … . Overall, this edited volume provides a good reference to the current state of bioinformatics-related databases and as an introduction to the more common machine-learning techniques in bioinformatics.” (Iddo Friedberg, The Quarterly Review of Biology, Vol. 86, December, 2011)