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Data Mining Techniques for the Life Sciences By Oliviero Carugo

Data Mining Techniques for the Life Sciences by Oliviero Carugo

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Summary

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.

Data Mining Techniques for the Life Sciences Summary

Data Mining Techniques for the Life Sciences by Oliviero Carugo

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.

Data Mining Techniques for the Life Sciences Reviews

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)

Table of Contents

Part I: Databases 1. Nucleic Acid Sequence and Structure Databases Stefan Washietl and Ivo L. Hofacker 2. Genomic Databases and Resources at the National Center for Biotechnology Information Tatiana Tatusova 3. Protein Sequence Databases Michael Rebhan 4. Protein Structure Databases Roman A. Laskowski 5. Protein Domain Architectures Nicola J. Mulder 6. Thermodynamic Database for Proteins: Features and Applications M. Michael Gromiha and Akinori Sarai 7. Enzyme Databases Dietmar Schomburg and Ida Schomburg 8. Biomolecular Pathway Databases Hong Sain Ooi, Georg Schneider, Teng-Ting Lim, Ying-Leong Chan, Birgit Eisenhaber, and Frank Eisenhaber 9. Databases of Protein-Protein Interactions and Complexes Hong Sain Ooi, Georg Schneider, Ying-Leong Chan, Teng-Ting Lim, Birgit Eisenhaber, and Frank Eisenhaber Part II: Data Mining Techniques 10. Proximity Measures for Cluster Analysis Oliviero Carugo 11. Clustering Criteria and Algorithms Oliviero Carugo 12. Neural Networks Zheng Rong Yang 13. A User's Guide to Support Vector Machines Asa Ben-Hur and Jason Weston 14. Hidden Markov Models in Biology Claus Vogl and Andreas Futschik Part III: Database Annotations and Predictions 15. Integrated Tools for Biomolecular Sequence-Based Function Prediction as Exemplified by the ANNOTATOR Software Environment Georg Schneider, Michael Wildpaner, Fernanda L. Sirota, Sebastian Maurer-Stroh, Birgit Eisenhaber, and Frank Eisenhaber 16. Computational Methods for ab initio and Comparative Gene Finding Ernesto Picardi and Graziano Pesole 17. Sequence and Structure Analysis of NoncodingRNAs Stefan Washietl 18. Conformational Disorder Sonia Longhi, Philippe Lieutaud, and Bruno Canard 19. Protein Secondary Structure Prediction Walter Pirovano and Jaap Heringa 20. Analysis and Prediction of Protein Quaternary Structure Anne Poupon and Joel Janin 21. Prediction of Posttranslational Modification of Proteins from Their Amino Acid Sequence Birgit Eisenhaber and Frank Eisenhaber 22. Protein Crystallizability Pawel Smialowski and Dmitrij Frishman

Additional information

NPB9781603272407
9781603272407
1603272402
Data Mining Techniques for the Life Sciences by Oliviero Carugo
New
Hardback
Humana Press Inc.
2009-12-14
407
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
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