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Data Mining Techniques for the Life Sciences

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Cover of 'Data Mining Techniques for the Life Sciences'

Table of Contents

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    Book Overview
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    Chapter 1 Data Mining Techniques for the Life Sciences
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    Chapter 2 Protein Structure Databases.
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    Chapter 3 The MIntAct Project and Molecular Interaction Databases.
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    Chapter 4 Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants.
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    Chapter 5 Classification and Exploration of 3D Protein Domain Interactions Using Kbdock.
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    Chapter 6 Data Mining of Macromolecular Structures.
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    Chapter 7 Criteria to Extract High-Quality Protein Data Bank Subsets for Structure Users.
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    Chapter 8 Homology-Based Annotation of Large Protein Datasets.
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    Chapter 9 Data Mining Techniques for the Life Sciences
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    Chapter 10 Improving the Accuracy of Fitted Atomic Models in Cryo-EM Density Maps of Protein Assemblies Using Evolutionary Information from Aligned Homologous Proteins.
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    Chapter 11 Systematic Exploration of an Efficient Amino Acid Substitution Matrix: MIQS.
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    Chapter 12 Promises and Pitfalls of High-Throughput Biological Assays.
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    Chapter 13 Data Mining Techniques for the Life Sciences
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    Chapter 14 Predicting Conformational Disorder.
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    Chapter 15 Classification of Protein Kinases Influenced by Conservation of Substrate Binding Residues.
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    Chapter 16 Spectral-Statistical Approach for Revealing Latent Regular Structures in DNA Sequence.
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    Chapter 17 Protein Crystallizability.
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    Chapter 18 Data Mining Techniques for the Life Sciences
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    Chapter 19 Data Mining Techniques for the Life Sciences
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    Chapter 20 Functional Analysis of Metabolomics Data.
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    Chapter 21 Data Mining Techniques for the Life Sciences
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    Chapter 22 A Broad Overview of Computational Methods for Predicting the Pathophysiological Effects of Non-synonymous Variants.
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    Chapter 23 Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.
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    Chapter 24 Protein Residue Contacts and Prediction Methods.
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    Chapter 25 The Recipe for Protein Sequence-Based Function Prediction and Its Implementation in the ANNOTATOR Software Environment.
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    Chapter 26 Data Mining Techniques for the Life Sciences
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    Chapter 27 Data Mining Techniques for the Life Sciences
Attention for Chapter 17: Protein Crystallizability.
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Chapter title
Protein Crystallizability.
Chapter number 17
Book title
Data Mining Techniques for the Life Sciences
Published in
Methods in molecular biology, January 2016
DOI 10.1007/978-1-4939-3572-7_17
Pubmed ID
Book ISBNs
978-1-4939-3570-3, 978-1-4939-3572-7
Authors

Pawel Smialowski, Philip Wong

Editors

Oliviero Carugo, Frank Eisenhaber

Abstract

Obtaining diffracting quality crystals remains a major challenge in protein structure research. We summarize and compare methods for selecting the best protein targets for crystallization, construct optimization and crystallization condition design. Target selection methods are divided into algorithms predicting the chance of successful progression through all stages of structural determination (from cloning to solving the structure) and those focusing only on the crystallization step. We tried to highlight pros and cons of different approaches examining the following aspects: data size, redundancy and representativeness, overfitting during model construction, and results evaluation. In summary, although in recent years progress was made and several sequence properties were reported to be relevant for crystallization, the successful prediction of protein crystallization behavior and selection of corresponding crystallization conditions continue to challenge structural researchers.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Mexico 1 5%
Unknown 21 95%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 7 32%
Researcher 5 23%
Student > Bachelor 3 14%
Student > Postgraduate 2 9%
Other 1 5%
Other 4 18%
Readers by discipline Count As %
Agricultural and Biological Sciences 8 36%
Biochemistry, Genetics and Molecular Biology 4 18%
Chemistry 2 9%
Medicine and Dentistry 2 9%
Immunology and Microbiology 2 9%
Other 4 18%