Data Mining in Biomedicine (Hardcover)

This volume presents an extensive collection of chapters covering various aspects of the exciting and important research area of data mining techniques in biomedicine. The topics include: - new approaches for the analysis of biomedical data, - applications of data mining techniques to real-life problems in medical practice, - comprehensive reviews of recent trends in the field.

The book addresses the problems arising in fundamental areas of biomedical research, such as genomics, proteomics, protein characterization, and neuroscience.

This volume would be of interest to scientists and practitioners working in the field of biomedicine, as well as related areas of engineering, mathematics, and computer science. It can also be helpful to graduate students and young researchers looking for new exciting directions in their work. Since each chapter can be read independently, readers interested in specific problems and applications may find the material of certain chapters useful.

Table of Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix
List of Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Part I Recent Methodological Developments for Data Mining
Problems in Biomedicine
Pattern-Based Discriminants in the Logical Analysis of Data
Sorin Alexe, Peter L. Hammer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Exploring Microarray Data with Correspondence Analysis
Stanislav Busygin, Panos M. Pardalos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
An Ensemble Method of Discovering Sample Classes Using
Gene Expression Profiling
Dechang Chen, Zhe Zhang, Zhenqiu Liu, Xiuzhen Cheng . . . . . . . . . . . . . . 39
CpG Island Identification with Higher Order and Variable
Order Markov Models
Zhenqiu Liu, Dechang Chen, Xue-wen Chen . . . . . . . . . . . . . . . . . . . . . . . . . 47
Data Mining Algorithms for Virtual Screening of Bioactive
Compounds
Mukund Deshpande, Michihiro Kuramochi, George Karypis . . . . . . . . . . . . 59
Sparse Component Analysis: a New Tool for Data Mining
Pando Georgiev, Fabian Theis, Andrzej Cichocki, Hovagim Bakardjian . . 91
Data Mining Via Entropy and Graph Clustering
Anthony Okafor, Panos Pardalos, Michelle Ragle . . . . . . . . . . . . . . . . . . . . 117

Molecular Biology and Pooling Design
Weili Wu, Yingshu Li, Chih-hao Huang, Ding-Zhu Du . . . . . . . . . . . . . . . . 133
An Optimization Approach to Identify the Relationship
between Features and Output of a Multi-label Classifier
Musa Mammadov, Alex Rubinov, John Yearwood . . . . . . . . . . . . . . . . . . . . . 141
Classifying Noisy and Incomplete Medical Data by a
Differential Latent Semantic Indexing Approach
Liang Chen, Jia Zeng, Jian Pei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
Ontology Search and Text Mining of MEDLINE Database
Hyunki Kim, Su-Shing Chen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
Part II Data Mining Techniques in Disease Diagnosis
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 . . . . . . 193
Diagnosis of Alport Syndrome by Pattern Recognition
Techniques
Giacomo Patrizi, Gabriella Addonisio, Costas Giannakakis, Andrea
Onetti Muda, Gregorio Patrizi, Tullio Faraggiana . . . . . . . . . . . . . . . . . . . . 209
Clinical Analysis of the Diagnostic Classification of Geriatric
Disorders
Giacomo Patrizi, Gregorio Patrizi, Luigi Di Cioccio, Claudia Bauco . . . . 231
Part III Data Mining Studies in Genomics and Proteomics
A Hybrid Knowledge Based-Clustering Multi-Class SVM
Approach for Genes Expression Analysis
Budi Santosa, Tyrrell Conway, Theodore Trafalis . . . . . . . . . . . . . . . . . . . . 261
Mathematical Programming Formulations for Problems in
Genomics and Proteomics
Cl’audio N. Meneses, Carlos A.S. Oliveira, Panos M. Pardalos . . . . . . . . . 275
Inferring the Origin of the Genetic Code
Maria Luisa Chiusano, Luigi Frusciante, Gerardo Toraldo. . . . . . . . . . . . . 291
Deciphering the Structures of Genomic DNA Sequences
Using Recurrence Time Statistics
Jian-Bo Gao, Yinhe Cao, Wen-wen Tung . . . . . . . . . . . . . . . . . . . . . . . . . . . 321

Clustering Proteomics Data Using Bayesian Principal
Component Analysis
Halima Bensmail, O. John Semmes, Abdelali Haoudi . . . . . . . . . . . . . . . . . 339
Bioinformatics for Traumatic Brain Injury: Proteomic Data
Mining
Su-Shing Chen, William E. Haskins, Andrew K. Ottens, Ronald L.
Hayes, Nancy Denslow, Kevin K.W. Wang . . . . . . . . . . . . . . . . . . . . . . . . . . 363
Part IV Characterization and Prediction of Protein Structure
Computational Methods for Protein Fold Prediction: an
Ab-initio Topological Approach
G. Ceci, A. Mucherino, M. D’Apuzzo, D. Di Serafino, S. Costantini,
A. Facchiano, G. Colonna. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391
A Topological Characterization of Protein Structure
Bala Krishnamoorthy, Scott Provan, Alexander Tropsha . . . . . . . . . . . . . . . 431
Part V Applications of Data Mining Techniques to Brain Dynamics
Studies
Data Mining in EEG: Application to Epileptic Brain
Disorders
W. Chaovalitwongse, P.M. Pardalos, L.D. Iasemidis, W.
Suharitdamrong, D.-S. Shiau, L.K. Dance, O.A. Prokopyev, V.L.
Boginski, P.R. Carney, J.C. Sackellares . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459
Information Flow in Coupled Nonlinear Systems: Application
to the Epileptic Human Brain
S. Sabesan, K. Narayanan, A. Prasad, L. D. Iasemidis, A. Spanias, K.
Tsakalis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 483
Reconstruction of Epileptic Brain Dynamics Using Data
Mining Techniques
Panos M. Pardalos, Vitaliy A. Yatsenko . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505
Automated Seizure Prediction Algorithm and its Statistical
Assessment: A Report from Ten Patients
D.-S. Shiau, L.D. Iasemidis, M.C.K. Yang, P.M. Pardalos, P.R.
Carney, L.K. Dance, W. Chaovalitwongse, J.C. Sackellares . . . . . . . . . . . 517
Seizure Predictability in an Experimental Model of Epilepsy
S.P. Nair, D.-S. Shiau, L.D. Iasemidis, W.M. Norman, P.M. Pardalos,
J.C. Sackellares, P.R. Carney . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 535

Network-Based Techniques in EEG Data Analysis and
Epileptic Brain Modeling
Oleg A. Prokopyev, Vladimir L. Boginski, Wanpracha Chaovalitwongse,
Panos M. Pardalos, J. Chris Sackellares, Paul R. Carney . . . . . . . . . . . . 559
Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 575