Data Mining Techniques for the Life Sciences

ISBN-13: 9781493956883 / Angielski / Miękka / 2016 / 407 str.

Oliviero Carugo; Frank Eisenhaber

Opis

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.

Recenzje

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)

Spis treści

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

Blurb

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.