Hosszú leírás:

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)

Tartalomjegyzék:

Recent Methodological Developments for Data Mining Problems in Biomedicine.- Pattern-Based Discriminants in the Logical Analysis of Data.- Exploring Microarray Data with Correspondence Analysis.- An Ensemble Method of Discovering Sample Classes Using Gene Expression Profiling.- CpG Island Identification with Higher Order and Variable Order Markov Models.- Data Mining Algorithms for Virtual Screening of Bioactive Compounds.- Sparse Component Analysis: a New Tool for Data Mining.- Data Mining Via Entropy and Graph Clustering.- Molecular Biology and Pooling Design.- An Optimization Approach to Identify the Relationship between Features and Output of a Multi-label Classifier.- Classifying Noisy and Incomplete Medical Data by a Differential Latent Semantic Indexing Approach.- Ontology Search and Text Mining of MEDLINE Database.- Data Mining Techniques in Disease Diagnosis.- Logical Analysis of Computed Tomography Data to Differentiate Entities of Idiopathic Interstitial Pneumonias.- Diagnosis of Alport Syndrome by Pattern Recognition Techniques.- Clinical Analysis of the Diagnostic Classification of Geriatric Disorders.- Data Mining Studies in Genomics and Proteomics.- A Hybrid Knowledge Based-Clustering Multi-Class SVM Approach for Genes Expression Analysis.- Mathematical Programming Formulations for Problems in Genomics and Proteomics.- Inferring the Origin of the Genetic Code.- Deciphering the Structures of Genomic DNA Sequences Using Recurrence Time Statistics.- Clustering Proteomics Data Using Bayesian Principal Component Analysis.- Bioinformatics for Traumatic Brain Injury: Proteomic Data Mining.- Characterization and Prediction of Protein Structure.- Computational Methods for Protein Fold Prediction: an Ab-initio Topological Approach.- A Topological Characterization of Protein Structure.- Applications of Data Mining Techniques to Brain Dynamics Studies.- Data Mining in EEG: Application to Epileptic Brain Disorders.- Information Flow in Coupled Nonlinear Systems: Application to the Epileptic Human Brain.- Reconstruction of Epileptic Brain Dynamics Using Data Mining Techniques.- Automated Seizure Prediction Algorithm and its Statistical Assessment: A Report from Ten Patients.- Seizure Predictability in an Experimental Model of Epilepsy.- Network-Based Techniques in EEG Data Analysis and Epileptic Brain Modeling.