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Data Mining in Biomedicine

  • Book
  • © 2007

Overview

Editors:
  1. Panos M. Pardalos

    1. University of Florida, Gainesville

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    You can also search for this editor in PubMed   Google Scholar

  2. Vladimir L. Boginski

    1. Florida State University, Tallahassee

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    You can also search for this editor in PubMed   Google Scholar

  3. Alkis Vazacopoulos

    1. Dash Optimization, Englewood Cliffs

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Demonstrates how new data mining methodologies are successfully applied in real-life biomedical practice, which makes it attractive to both researchers and practitioners
  • Includes supplementary material: sn.pub/extras

Part of the book series: Springer Optimization and Its Applications (SOIA, volume 7)

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Table of contents (28 chapters)

  1. Front Matter

    Pages i-xvii
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  2. Recent Methodological Developments for Data Mining Problems in Biomedicine

    1. Front Matter

      Pages 1-1
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    2. Pattern-Based Discriminants in the Logical Analysis of Data

      • Sorin Alexe, Peter L. Hammer
      Pages 3-23

    3. Exploring Microarray Data with Correspondence Analysis

      • Stanislav Busygin, Panos M. Pardalos
      Pages 25-37

    4. An Ensemble Method of Discovering Sample Classes Using Gene Expression Profiling

      • Dechang Chen, Zhe Zhang, Zhenqiu Liu, Xiuzhen Cheng
      Pages 39-46

    5. CpG Island Identification with Higher Order and Variable Order Markov Models

      • Zhenqiu Liu, Dechang Chen, Xue-wen Chen
      Pages 47-57

    6. Data Mining Algorithms for Virtual Screening of Bioactive Compounds

      • Mukund Deshpande, Michihiro Kuramochi, George Karypis
      Pages 59-90

    7. Sparse Component Analysis: a New Tool for Data Mining

      • Pando Georgiev, Fabian Theis, Andrzej Cichocki, Hovagim Bakardjian
      Pages 91-116

    8. Data Mining Via Entropy and Graph Clustering

      • Anthony Okafor, Panos Pardalos, Michelle Ragle
      Pages 117-131

    9. Molecular Biology and Pooling Design

      • Weili Wu, Yingshu Li, Chih-hao Huang, Ding-Zhu Du
      Pages 133-139

    10. An Optimization Approach to Identify the Relationship between Features and Output of a Multi-label Classifier

      • Musa Mammadov, Alex Rubinov, John Yearwood
      Pages 141-167

    11. Classifying Noisy and Incomplete Medical Data by a Differential Latent Semantic Indexing Approach

      • Liang Chen, Jia Zeng, Jian Pei
      Pages 169-176

    12. Ontology Search and Text Mining of MEDLINE Database

      • Hyunki Kim, Su-Shing Chen
      Pages 177-189

  3. Data Mining Techniques in Disease Diagnosis

    1. Front Matter

      Pages 191-191
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    2. Logical Analysis of Computed Tomography Data to Differentiate Entities of Idiopathic Interstitial Pneumonias

      • M. W. Brauner, N. Brauner, P. L. Hammer, I. Lozina, D. Valeyre
      Pages 193-208

    3. Diagnosis of Alport Syndrome by Pattern Recognition Techniques

      • Giacomo Patrizi, Gabriella Addonisio, Costas Giannakakis, Andrea Onetti Muda, Gregorio Patrizi, Tullio Faraggiana
      Pages 209-230

    4. Clinical Analysis of the Diagnostic Classification of Geriatric Disorders

      • Giacomo Patrizi, Gregorio Patrizi, Luigi Di Cioccio, Claudia Bauco
      Pages 231-258

  4. Data Mining Studies in Genomics and Proteomics

    1. Front Matter

      Pages 259-259
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    2. A Hybrid Knowledge Based-Clustering Multi-Class SVM Approach for Genes Expression Analysis

      • Budi Santosa, Tyrrell Conway, Theodore Trafalis
      Pages 261-274

    3. Mathematical Programming Formulations for Problems in Genomics and Proteomics

      • Cláudio N. Meneses, Carlos A. S. Oliveira, Panos M. Pardalos
      Pages 275-290
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Keywords

Reviews

From the reviews:

"This book is an in-depth look at ‘the development of appropriate methods for extracting useful information’ from data in biomedicine. … is aimed at scientists and practitioners in the fields of biomedicine, engineering, mathematics, and computer science as well as graduate students and is appropriate for a variety of readers. … A well compiled volume on the application of data mining to biomedicine, this book will be a welcome addition to the literature." (Nicole Mitchell, Doody’s Review Service, August, 2008)

Editors and Affiliations

  • University of Florida, Gainesville

    Panos M. Pardalos

  • Florida State University, Tallahassee

    Vladimir L. Boginski

  • Dash Optimization, Englewood Cliffs

    Alkis Vazacopoulos

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