Ebook: Data Mining for Biomarker Discovery

Biomarker discovery is an important area of biomedical research that may lead to significant breakthroughs in disease analysis and targeted therapy. Biomarkers are biological entities whose alterations are measurable and are characteristic of a particular biological condition. Discovering, managing, and interpreting knowledge of new biomarkers are challenging and attractive problems in the emerging field of biomedical informatics.

This volume is a collection of state-of-the-art research into the application of data mining to the discovery and analysis of new biomarkers. Presenting new results, models and algorithms, the included contributions focus on biomarker data integration, information retrieval methods, and statistical machine learning techniques.

This volume is intended for students, and researchers in bioinformatics, proteomics, and genomics, as well engineers and applied scientists interested in the interdisciplinary application of data mining techniques.

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Data Mining for Biomarker Discovery is designed to motivate collaboration and discussion among various disciplines and will be of interest to students and researchers in engineering, computer science, applied mathematics, medicine, and anyone interested in the interdisciplinary application of data mining techniques. Biomarker discovery is an important area of biomedical research that can lead to significant breakthroughs in disease analysis and targeted therapy. Moreover, the discovery and management of new biomarkers is a challenging and attractive problem in the emerging field of biomedical informatics.

This volume is a collection of state-of-the-art research from select participants of the “International Conference on Biomedical Data and Knowledge Mining: Towards Biomarker Discovery,” held July 7-9, 2010 in Chania, Greece. Contributions focus on biomarker data integration, information retrieval methods, and statistical machine learning techniques, all presented with new results, models, and algorithms.

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Data Mining for Biomarker Discovery is designed to motivate collaboration and discussion among various disciplines and will be of interest to students and researchers in engineering, computer science, applied mathematics, medicine, and anyone interested in the interdisciplinary application of data mining techniques. Biomarker discovery is an important area of biomedical research that can lead to significant breakthroughs in disease analysis and targeted therapy. Moreover, the discovery and management of new biomarkers is a challenging and attractive problem in the emerging field of biomedical informatics.

This volume is a collection of state-of-the-art research from select participants of the “International Conference on Biomedical Data and Knowledge Mining: Towards Biomarker Discovery,” held July 7-9, 2010 in Chania, Greece. Contributions focus on biomarker data integration, information retrieval methods, and statistical machine learning techniques, all presented with new results, models, and algorithms.

Content:
Front Matter....Pages i-xiv
Data Mining Strategies Applied in Brain Injury Models....Pages 1-13
Application of Decomposition Methods in the Filtering of Event-Related Potentials....Pages 15-29
EEG Features as Biomarkers for Discrimination of Preictal States....Pages 31-56
Using Relative Power Asymmetry as a Biomarker for Classifying Psychogenic Nonepileptic Seizure and Complex Partial Seizure Patients....Pages 57-77
Classification of Tree and Network Topology Structures in Medical Images....Pages 79-90
A Framework for Multimodal Imaging Biomarker Extraction with Application to Brain MRI....Pages 91-116
A Statistical Diagnostic Decision Support Tool Using Magnetic Resonance Spectroscopy Data....Pages 117-142
Data Mining for Cancer Biomarkers with Raman Spectroscopy....Pages 143-168
Nonlinear Recognition Methods for Oncological Pathologies....Pages 169-185
Studying Connectivity Properties in Human Protein–Protein Interaction Network in Cancer Pathway....Pages 187-197
Modelling of Oral Cancer Progression Using Dynamic Bayesian Networks....Pages 199-212
Neuromuscular Alterations of Upper Airway Muscles in Patients with OSAS: Radiological and Histopathological Findings....Pages 213-226
Data Mining System Applied to Population Databases for Studies on Lung Cancer....Pages 227-246

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Data Mining for Biomarker Discovery is designed to motivate collaboration and discussion among various disciplines and will be of interest to students and researchers in engineering, computer science, applied mathematics, medicine, and anyone interested in the interdisciplinary application of data mining techniques. Biomarker discovery is an important area of biomedical research that can lead to significant breakthroughs in disease analysis and targeted therapy. Moreover, the discovery and management of new biomarkers is a challenging and attractive problem in the emerging field of biomedical informatics.

This volume is a collection of state-of-the-art research from select participants of the “International Conference on Biomedical Data and Knowledge Mining: Towards Biomarker Discovery,” held July 7-9, 2010 in Chania, Greece. Contributions focus on biomarker data integration, information retrieval methods, and statistical machine learning techniques, all presented with new results, models, and algorithms.

Content:
Front Matter....Pages i-xiv
Data Mining Strategies Applied in Brain Injury Models....Pages 1-13
Application of Decomposition Methods in the Filtering of Event-Related Potentials....Pages 15-29
EEG Features as Biomarkers for Discrimination of Preictal States....Pages 31-56
Using Relative Power Asymmetry as a Biomarker for Classifying Psychogenic Nonepileptic Seizure and Complex Partial Seizure Patients....Pages 57-77
Classification of Tree and Network Topology Structures in Medical Images....Pages 79-90
A Framework for Multimodal Imaging Biomarker Extraction with Application to Brain MRI....Pages 91-116
A Statistical Diagnostic Decision Support Tool Using Magnetic Resonance Spectroscopy Data....Pages 117-142
Data Mining for Cancer Biomarkers with Raman Spectroscopy....Pages 143-168
Nonlinear Recognition Methods for Oncological Pathologies....Pages 169-185
Studying Connectivity Properties in Human Protein–Protein Interaction Network in Cancer Pathway....Pages 187-197
Modelling of Oral Cancer Progression Using Dynamic Bayesian Networks....Pages 199-212
Neuromuscular Alterations of Upper Airway Muscles in Patients with OSAS: Radiological and Histopathological Findings....Pages 213-226
Data Mining System Applied to Population Databases for Studies on Lung Cancer....Pages 227-246
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