Chapter 1 Data Mining Techniques for the Life Sciences

Chapter 2 Protein Structure Databases.

Chapter 3 The MIntAct Project and Molecular Interaction Databases.

Chapter 4 Applications of Protein Thermodynamic Database for Understanding Protein Mutant Stability and Designing Stable Mutants.

Chapter 5 Classification and Exploration of 3D Protein Domain Interactions Using Kbdock.

Chapter 6 Data Mining of Macromolecular Structures.

Chapter 7 Criteria to Extract High-Quality Protein Data Bank Subsets for Structure Users.

Chapter 9 Data Mining Techniques for the Life Sciences

Chapter 10 Improving the Accuracy of Fitted Atomic Models in Cryo-EM Density Maps of Protein Assemblies Using Evolutionary Information from Aligned Homologous Proteins.

Chapter 11 Systematic Exploration of an Efficient Amino Acid Substitution Matrix: MIQS.

Chapter 12 Promises and Pitfalls of High-Throughput Biological Assays.

Chapter 13 Data Mining Techniques for the Life Sciences

Chapter 14 Predicting Conformational Disorder.

Chapter 15 Classification of Protein Kinases Influenced by Conservation of Substrate Binding Residues.

Chapter 16 Spectral-Statistical Approach for Revealing Latent Regular Structures in DNA Sequence.

Chapter 17 Protein Crystallizability.

Chapter 18 Data Mining Techniques for the Life Sciences

Chapter 19 Data Mining Techniques for the Life Sciences

Chapter 20 Functional Analysis of Metabolomics Data.

Chapter 21 Data Mining Techniques for the Life Sciences

Chapter 22 A Broad Overview of Computational Methods for Predicting the Pathophysiological Effects of Non-synonymous Variants.

Chapter 23 Recommendation Techniques for Drug-Target Interaction Prediction and Drug Repositioning.

Chapter 24 Protein Residue Contacts and Prediction Methods.

Chapter 25 The Recipe for Protein Sequence-Based Function Prediction and Its Implementation in the ANNOTATOR Software Environment.

Chapter 26 Data Mining Techniques for the Life Sciences

Chapter 27 Data Mining Techniques for the Life Sciences