Data Mining Techniques for the Life Sciences

In this book, experts in the field contribute valuable information about the sources of information and the techniques used for "mining" new insights out of databases. The book covers a wide range of biological systems and in silico approaches.

Most life science researchers will agree that biology is not a truly theoretical branch of science. The hype around computational biology and bioinformatics beginning in the nineties of the 20th century was to be short lived (1, 2). When almost no value of practical importance such as the optimal dose of a drug or the three-dimensional structure of an orphan protein can be computed from fundamental principles, it is still more straightforward to determine them experimentally. Thus, experiments and observationsdogeneratetheoverwhelmingpartofinsightsintobiologyandmedicine. The extrapolation depth and the prediction power of the theoretical argument in life sciences still have a long way to go. Yet, two trends have qualitatively changed the way how biological research is done today. The number of researchers has dramatically grown and they, armed with the same protocols, have produced lots of similarly structured data. Finally, high-throu- put technologies such as DNA sequencing or array-based expression profiling have been around for just a decade. Nevertheless, with their high level of uniform data generation, they reach the threshold of totally describing a living organism at the biomolecular level for the first time in human history. Whereas getting exact data about living systems and the sophistication of experimental procedures have primarily absorbed the minds of researchers previously, the weight increasingly shifts to the problem of interpreting accumulated data in terms of biological function and bio- lecular mechanisms.

An easily accessible reference book for computational data mining, ranging from databases, to computational details, and to modern applications Covers a wide range of biological systems and in silico approaches Presents the exciting interface between computational and experimental approaches of molecular biology Serves as a comprehensive guide to designing and running systematic and large scale computational analyses of biological data Outlines the process of turning a series of observations into a network of relationships Includes supplementary material: sn.pub/extras

Klappentext

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.

Inhalt
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 ProteinProtein 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.