Data mining techniques for the life sciences

著者

書誌事項

Data mining techniques for the life sciences

edited by Oliviero Carugo, Frank Eisenhaber

（Methods in molecular biology / John M. Walker, series editor, v. 609）（Springer protocols）

Humana Press, c2010

大学図書館所蔵 件 / 全1件

この図書・雑誌をさがす

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

「Nielsen BookData」 より

関連文献： 2件中  1-2を表示

詳細情報

ページトップへ