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## Details

Genre/Form: | Electronic books |
---|---|

Additional Physical Format: | Print version: MacCuish, John D. Clustering in bioinformatics and drug discovery. Boca Raton, Florida : CRC Press, [2011] 214 pages ; 25 cm. (DLC) 11005342 (OCoLC)643443438 |

Material Type: | Document, Internet resource |

Document Type: | Internet Resource, Computer File |

All Authors / Contributors: |
John D MacCuish; Norah E MacCuish |

ISBN: | 9781439816790 1439816794 |

OCLC Number: | 903970440 |

Description: | 1 online resource (235 pages) : illustrations. |

Contents: | Cover; Title; Copyright; Contents; List of Figures; List of Tables; Preface; Acknowledgments; About the Authors; List of Symbols; Foreword; Chapter 1: Introduction; Chapter 2: Data; Chapter 3: Clustering Forms; Chapter 4: Partitional Algorithms; Chapter 5: Cluster Sampling Algorithms; Chapter 6: Hierarchical Algorithms; Chapter 7: Hybrid Algorithms; Chapter 8: Asymmetry; Chapter 9: Ambiguity; Chapter 10: Validation; Chapter 11: Large Scale and Parallel Algorithms; Chapter 12: Appendices; Bibliography. |

Series Title: | Chapman and Hall/CRC mathematical & computational biology series. |

Responsibility: | John D. MacCuish, Norah E. MacCuish. |

### Abstract:

## Reviews

*Editorial reviews*

Publisher Synopsis

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. IntroductionHistory Bioinformatics and Drug Discovery Statistical Learning Theory and Exploratory Data Analysis Clustering Algorithms Computational ComplexityDataTypesNormalization and Scaling Transformations Formats Data Matrices Measures of SimilarityProximity Matrices Symmetric MatricesDimensionality, Components, DiscriminantsGraph TheoryClustering FormsPartitional HierarchicalMixture Models Sampling Overlapping Fuzzy Self-Organizing HybridsPartitional AlgorithmsK-MeansJarvis-Patrick Spectral Clustering Self-Organizing MapsCluster Sampling AlgorithmsLeader Algorithms Taylor-Butina AlgorithmHierarchical AlgorithmsAgglomerativeDivisiveHybrid AlgorithmsSelf-Organizing Tree Algorithm Divisive Hierarchical K-Means Exclusion Region Hierarchies BiclusteringAsymmetryMeasuresAlgorithmsAmbiguityDiscrete Valued Data Types Precision Ties in Proximity Measure Probability and Distributions Algorithm Decision Ambiguity Overlapping Clustering Algorithms Based on AmbiguityValidationValidation MeasuresVisualization ExampleLarge Scale and Parallel AlgorithmsLeader and Leader-Follower Algorithms Taylor-Butina K-Means and VariantsExamplesAppendicesBibliographyA Glossary and Exercises appear at the end of each chapter. John trained in computer science and has been involved with data mining and statistical analysis; Norah trained as a theoretical physical chemist and has mostly worked for pharmaceutical companies on drug discovery. They run a company that merges their fields, and it is that overlap that they describe here. They explain how cluster analysis, an exploratory data analysis tool, is used in bioinformatics and cheminformatics as they relate to drug discovery. The goal is for practitioners to be aware of the relative merits of clustering methods with the data they have at hand.-SciTech Book News, February 2011â ¦ In this volume, the authors present sufficient options so that the user can choose the appropriate method for their data. â ¦ Practitioners in the pharmaceutical industry need an expert guide, which the authors of this book provide, to extract the most information from their data. Those of us who learned their clustering from Anderberg, Sokal and Sneath, and Willett now have a valuable additional resource suitable for the 21st century.-From the Foreword by John Bradshaw, Barley, Hertfordshire, UK 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. 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. 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. 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. Read more...

*User-contributed reviews*