Description
Clustering in Bioinformatics and Drug Discovery
Chapman & Hall/CRC Computational Biology Series
Authors: MacCuish John David, MacCuish Norah E.
Language: EnglishSubjects for Clustering in Bioinformatics and Drug Discovery:
Keywords
Clustering Algorithms; Agglomerative Hierarchical Algorithms; cluster analysis; Exclusion Region; bioinformatics; Data Items; drug discovery; Som; cheminformatics; Data Set; statistical learning theory; Hierarchical Algorithms; exploratory data analysis; Tanimoto Similarity; graph theory; Cluster Members; self-organizing maps; Smile; cluster sampling algorithms; Complete Link; K-means; Smile String; biclustering; Bit Strings; parallel algorithms; Binary Strings; parallelization; Farey Sequence; combinatorial library design; SOTA; compound databases; Complete Link Clustering; Proximity Matrix; Bell Number; SCC; Hierarchical Clustering Algorithm; Single Link Clustering; Arbitrary Centers
Approximative price 74.82 €
In Print (Delivery period: 14 days).
Add to cart the book of MacCuish John David, MacCuish Norah E.Publication date: 09-2019
· 15.6x23.4 cm · Paperback
Approximative price 111.58 €
In Print (Delivery period: 15 days).
Add to cart the book of MacCuish John David, MacCuish Norah E.Publication date: 11-2010
356 p. · 15.6x23.4 cm · Hardback
Description
/li>Contents
/li>Readership
/li>Biography
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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.
Introduction. Data. Clustering Forms. Partitional Algorithms. Cluster Sampling Algorithms. Hierarchical Algorithms. Hybrid Algorithms. Asymmetry. Ambiguity. Validation. Large Scale and Parallel Algorithms. Appendices. Bibliography.
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.