Clustering in Bioinformatics and Drug Discovery

Author:   John David MacCuish (Mesa Analytics & Computing, Inc., Santa Fe, New Mexico, USA) ,  Norah E. MacCuish (Mesa Analytics & Computing, Inc., Santa Fe, New Mexico, USA)
Publisher:   Taylor & Francis Ltd
ISBN:  

9781138374232


Pages:   244
Publication Date:   05 September 2019
Format:   Paperback
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Clustering in Bioinformatics and Drug Discovery


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Author:   John David MacCuish (Mesa Analytics & Computing, Inc., Santa Fe, New Mexico, USA) ,  Norah E. MacCuish (Mesa Analytics & Computing, Inc., Santa Fe, New Mexico, USA)
Publisher:   Taylor & Francis Ltd
Imprint:   CRC Press
ISBN:  

9781138374232


ISBN 10:   1138374237
Pages:   244
Publication Date:   05 September 2019
Audience:   College/higher education ,  Professional and scholarly ,  Tertiary & Higher Education ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Not yet available   Availability explained
This item is yet to be released. You can pre-order this item and we will dispatch it to you upon its release.

Table of Contents

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.

Reviews

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 2011 ... In this volume, the authors present sufficient options so that the user can choose the appropriate method for their data. ... 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


Author Information

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.

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