Data Mining Techniques for the Life Sciences
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Beschreibung
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.
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
Autor*in
Oliviero Carugo
Themen in »Data Mining Techniques for the Life Sciences«
Biological research Biomolecular data Clustering Computer-assisted data analysis Hidden Markov Model High-throughput technologies In silico Markov model National Center for Biotechnology Information algorithms cluster analysis data mining databases genome life sciences
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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)
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