Carugo O., Eisenhaber F. (Eds.) Data Mining Techniques for the Life Sciences

Humana Press, 2010.— 407 p.
Series: Methods in Molecular Biology,
ISBN: 978-1603272407, e-ISBN: 978-1603272414.Whereas getting exact data about living systems and sophisticated experimental procedures have primarily absorbed the minds of researchers previously, the development of high-throughput technologies has caused the weight to increasingly shift to the problem of interpreting accumulated data in terms of biological function and biomolecular mechanisms. In "Data Mining Techniques for the Life Sciences", experts in the field contribute valuable information about the sources of information and the techniques used for "mining" new insights out of databases. Beginning with a section covering the concepts and structures of important groups of databases for biomolecular mechanism research, the book then continues with sections on formal methods for analyzing biomolecular data and reviews of concepts for analyzing biomolecular sequence data in context with other experimental results that can be mapped onto genomes. As a volume of the highly successful Methods in Molecular Biology series, this work provides the kind of detailed description and implementation advice that is crucial for getting optimal results.
Authoritative and easy to reference, "Data Mining Techniques for the Life Sciences" seeks to aid students and researchers in the life sciences who wish to get a condensed introduction into the vital world of biological databases and their many applications.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
Proximity Measures for Cluster Analysis
Clustering Criteria and Algorithms
Neural Networks
A User’s Guide to Support Vector Machines
Hidden Markov Models in Biology
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