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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 24: Protein Residue Contacts and Prediction Methods.
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