Data Mining Techniques for the Life Sciences

1. Front Matter

   Pages i-xii

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2. Databases

   1. Front Matter

      Pages 1-1

      PDF

   2. Nucleic Acid Sequence and Structure Databases

      • Stefan Washietl, Ivo L. Hofacker

      Pages 3-15

   3. Genomic Databases and Resources at the National Center for Biotechnology Information

      • Tatiana Tatusova

      Pages 17-44

   4. Protein Sequence Databases

      • Michael Rebhan

      Pages 45-57

   5. Protein Structure Databases

      • Roman A. Laskowski

      Pages 59-82

   6. Protein Domain Architectures

      • Nicola J. Mulder

      Pages 83-95

   7. Thermodynamic Database for Proteins: Features and Applications

      • M. Michael Gromiha, Akinori Sarai

      Pages 97-112

   8. Enzyme Databases

      • Dietmar Schomburg, Ida Schomburg

      Pages 113-128

   9. Biomolecular Pathway Databases

      • Hong Sain Ooi, Georg Schneider, Teng-Ting Lim, Ying-Leong Chan, Birgit Eisenhaber, Frank Eisenhaber

      Pages 129-144

   10. Databases of Protein–Protein Interactions and Complexes

       • Hong Sain Ooi, Georg Schneider, Ying-Leong Chan, Teng-Ting Lim, Birgit Eisenhaber, Frank Eisenhaber

       Pages 145-159

3. Data Mining Techniques

   1. Front Matter

      Pages 161-161

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   2. Proximity Measures for Cluster Analysis

      • Oliviero Carugo

      Pages 163-174

   3. Clustering Criteria and Algorithms

      • Oliviero Carugo

      Pages 175-196

   4. Neural Networks

      • Zheng Rong Yang

      Pages 197-222

   5. A User’s Guide to Support Vector Machines

      • Asa Ben-Hur, Jason Weston

      Pages 223-239

   6. Hidden Markov Models in Biology

      • Claus Vogl, Andreas Futschik

      Pages 241-253

4. Database Annotations and Predictions

   1. Front Matter

      Pages 255-255

      PDF

   2. 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, Frank Eisenhaber

      Pages 257-267

   3. Computational Methods for Ab Initio and Comparative Gene Finding

      • Ernesto Picardi, Graziano Pesole

      Pages 269-284

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.

• 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
• Systems Biology

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)

• Max F. Perutz Laboratories GmbH, Universität Wien, Wien, Austria

  Oliviero Carugo

• Research (A*STAR), Agency for Science & Technology, Singapore, Singapore

  Frank Eisenhaber

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