MacCuish Clustering in Bioinformatics and Drug Discovery

1. Auflage 2010
ISBN: 978-1-4398-1679-0
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 244 Seiten

Reihe: Chapman & Hall/CRC Mathematical & Computational Biology

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.

Zielgruppe

Researchers in chemoinformatics, drug discovery, computational biology, biostatistics, and bioinformatics; supplemental text for graduate students in chemoinformatics and bioinformatics.

Autoren/Hrsg.

MacCuish, John David

MacCuish, Norah E.

Fachgebiete

• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizinische Fachgebiete Pharmakologie, Toxikologie
• Naturwissenschaften Biowissenschaften Biowissenschaften

Inhaltsverzeichnis

Introduction
History

Bioinformatics and Drug Discovery

Statistical Learning Theory and Exploratory Data Analysis

Clustering Algorithms

Computational Complexity

Data
Types
Normalization and Scaling

Transformations

Formats

Data Matrices

Measures of Similarity
Proximity Matrices

Symmetric Matrices
Dimensionality, Components, Discriminants
Graph Theory

Clustering Forms
Partitional

Hierarchical
Mixture Models

Sampling

Overlapping

Fuzzy

Self-Organizing

Hybrids

Partitional Algorithms
K-Means
Jarvis–Patrick

Spectral Clustering

Self-Organizing Maps

Cluster Sampling Algorithms
Leader Algorithms

Taylor–Butina Algorithm

Hierarchical Algorithms
Agglomerative
Divisive

Hybrid Algorithms
Self-Organizing Tree Algorithm

Divisive Hierarchical K-Means

Exclusion Region Hierarchies

Biclustering

Asymmetry
Measures
Algorithms

Ambiguity
Discrete Valued Data Types

Precision

Ties in Proximity

Measure Probability and Distributions

Algorithm Decision Ambiguity

Overlapping Clustering Algorithms Based on Ambiguity
Validation
Validation Measures
Visualization

Example
Large Scale and Parallel Algorithms
Leader and Leader-Follower Algorithms

Taylor–Butina

K-Means and Variants
Examples
Appendices
Bibliography
A Glossary and Exercises appear at the end of each chapter.

Über Autor(innen)

John D. MacCuish is the founder and president of Mesa Analytics & Computing, Inc. He has co-authored several software patents and has worked on many image processing, data mining, and statistical modeling applications, including IRS fraud detection, credit card fraud detection, and automated reasoning systems for drug discovery.
Norah E. MacCuish is the chief science officer of Mesa Analytics & Computing, Inc., where she acts as a consultant in the areas of drug design and compound acquisition and as a developer of commercial chemical information software products. She earned her Ph.D. in theoretical physical chemistry from Cornell University.