04210cam a22002534a 4500008004100000020001800041082002000059100002200079245006200101260003200163300003700195490006900232504137600301520207901677650001903756650001603775650002103791650002103812650002603833650002103859650002703880650002603907700002303933100622s2011 flua b 001 0 eng a978143981678300a572.80285bM2C51 aMacCuish, John D.10aClustering in bioinformatics and drug discovery (with CD) aBoca RatonbCRC Pressc2011 a214 p.ewith CD Acc.No: CD0024880 aChapman & Hall/CRC mathematical and computational biology series aTable of Contents
1. Introduction
History
Bioinformatics and Drug Discovery
Statistical Learning Theory and Exploratory Data Analysis
Clustering Algorithms
Computational Complexity
2. Data
Types
Normalization and Scaling
Transformations
Formats
Data Matrices
Measures of Similarity
Proximity Matrices
Symmetric Matrices
Dimensionality, Components, Discriminants
Graph Theory
3. Clustering Forms
Partitional
Hierarchical
Mixture Models
Sampling
Overlapping
Fuzzy
Self-Organizing
Hybrids
4. Partitional Algorithms
K-Means
Jarvis–Patrick
Spectral Clustering
Self-Organizing Maps
5. Cluster Sampling Algorithms
Leader Algorithms
Taylor–Butina Algorithm
6. Hierarchical Algorithms
Agglomerative
Divisive
7. Hybrid Algorithms
Self-Organizing Tree Algorithm
Divisive Hierarchical K-Means
Exclusion Region Hierarchies
Biclustering
8. Asymmetry
Measures
Algorithms
9. Ambiguity
Discrete Valued Data Types
Precision
Ties in Proximity
Measure Probability and Distributions
Algorithm Decision Ambiguity
Overlapping Clustering Algorithms Based on Ambiguity
10. Validation
Validation Measures
Visualization
Example
11. Large Scale and Parallel Algorithms
Leader and Leader-Follower Algorithms
Taylor–Butina
K-Means and Variants
Examples
12. Appendices
13. Bibliography
14. A Glossary and Exercises appear at the end of each
chapter.
aFeatures
• Covers the clustering of small and large data sets, parallelization of clustering algorithms, validation and visualization, asymmetric clustering, and clustering ambiguity
• Presents over 20 algorithms in pseudocode
• Provides real-world examples from industrial settings, such as combinatorial library design and compound databases
• Contains exercises at the end of each chapter
• Offers primers on matrix algebra, probability theory, and number theory for those new to the mathematics of clustering
• Includes a DVD with color figures from the book
Solutions manual available upon qualifying course adoption.
Summary
With a DVD of color figures, Clustering in Bioinformatics and Drug Discovery provides an expert guide on extracting the most pertinent information from pharmaceutical and biomedical data. It offers a concise overview of common and recent clustering methods used in bioinformatics and drug discovery. Setting the stage for subsequent material, the first three chapters of the book introduce statistical learning theory, exploratory data analysis, clustering algorithms, different types of data, graph theory, and various clustering forms. In the following chapters on partitional, cluster sampling, and hierarchical algorithms, the book provides readers with enough detail to obtain a basic understanding of cluster analysis for bioinformatics and drug discovery. The remaining chapters cover more advanced methods, such as hybrid and parallel algorithms, as well as details related to specific types of data, including asymmetry, ambiguity, validation measures, and visualization. This book explores the application of cluster analysis in the areas of bioinformatics and cheminformatics as they relate to drug discovery. Clarifying the use and misuse of clustering methods, it helps readers understand the relative merits of these methods and evaluate results so that useful hypotheses can be developed and tested.
https://www.crcpress.com/Clustering-in-Bioinformatics-and-Drug-Discovery/MacCuish-MacCuish/p/book/9781439816783 0aBioinformatics 0aMathematics 0aDrug development 0aCluster analysis 0aComputational biology12aCluster analysis12aDrug discovery methods22aComputational biology1 aMacCuish, Norah E.