Data mining techniques for the life sciences
著者
書誌事項
Data mining techniques for the life sciences
(Methods in molecular biology / John M. Walker, series editor, v. 609)(Springer protocols)
Humana Press, c2010
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
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
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