04432cam a22002774a 4500
100622s2011 flua b 001 0 eng
9781439816783
572.80285
M2C5
MacCuish, John D.
338475
Clustering in bioinformatics and drug discovery (with CD)
Boca Raton
CRC Press
2011
214 p.
with CD Acc.No: CD002488
Chapman & Hall/CRC mathematical and computational biology series
Table 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.
Features
• 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
Bioinformatics
338476
Mathematics
370
Drug development
338477
Cluster analysis
38323
Computational biology
73521
Cluster analysis
38323
Drug discovery methods
338478
Computational biology
73521
MacCuish, Norah E.
338479
ddc
BK
205217
205217
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NFIC
345333
VSL
VSL
Slot 1702 (2 Floor, East Wing)
2016-12-26
5
4550.96
Slot 1702 (2 Floor, East Wing)
572.80285 M2C5
193428
2016-12-26
5688.71
BK
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ddc
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NFIC
345629
VSL
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2017-01-02
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D002488
CD002488
2017-01-02
CD