Data Mining for Biomarker Discovery [electronic resource] /
Pardalos, Panos M.editor.(orcid)0000-0003-2824-101Xhttps://orcid.org/0000-0003-2824-101Xedt
Xanthopoulos, Petros.editor.
Zervakis, Michalis.editor.
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.
Description based upon print version of record.
Includes bibliographical references.
Preface -- 1. Data Mining Strategies Applied in Brain Injury Models (S. Mondello, F. Kobeissy, I. Fingers, Z. Zhang, R.L. Hayes, K.K.W. Wang) -- Application of Decomposition Methods in the Filtering of Event Related Potentials (K. Michalopoulos, V. Iordanidou, M. Zervakis) -- 3. EEG Features as Biomarkers for Discrimination of Pre-ictal states (A. Tsimpiris, D. Kugiumtzis) -- 4. Using Relative Power Asymmetry as a Biomarker for Classifying Psychogenic Non-epileptic Seizure and Complex Partial Seizure Patients (J.H. Chien, D.-S. Shiau, J.C. Sackellares, J.J. Halford, K.M. Kelly, P.M. Pardalos) -- 5. Classification of Tree and Network Topology Structures in Medical Images (A. Skoura, V. Megalooikonomou, A. Diamantopolous, G.C. Kagadis, D. Karnabatidis) -- 6. A Framework for Multi-Modal Imagin Biomarker Extraction with Application to Brain MRI (K. Maria, V. Sakkalis, N. Graf) -- 7. A Statistical Diagnostic Decision Support Tool Using Magnetic Resonance Spectroscopy Data (E. Tsolaki, E. Kousi, E. Kapsalaki, I. Dimou, K. Theodorou, G. C. Manikis, C. Kappas, I. Tsougos) -- 8. Data Mining for Cancer Biomarkers with Raman Spectroscopy (M.B.Fenn, V. Pappu) -- 9. Nonlinear Recognition Methods for Oncological Pathologies (G. Patrizi, V. Pietropaolo, A. Carbone, R. De Leone, L. Di Giacomo, V. Losaco, G. Patrizi) -- 10. Studying Connectivity Properties in Human Protein Interaction Network in Cancer Pathway (V. Tomaino, A. Arulselvan, P. Veltri, P.M. Pardalos) -- 11. Modeling of Oral Cancer Progression Using Dynamic Bayesian Networks (K.P. Exarchos, G. Rigas, Y. Golestsis, D.I. Fotiadis) -- 12. Neuromuscular Alterations of Upper Airway Muscles in Patients with OSAS Radiological and Histopathological Findings (P. Drakatos, D. Lykouras, F. Sampsonas, K. Karkoulias, K. Spiropoulos) -- 13. Data Mining System Applied to Population Databases for Studies on Lung Cancer (J. Pérez, F. Henriques, R. Santaolaya, O. Fragoso, A. Mexicano).
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.
Operations research.
Management science.
Data mining.
Health informatics.
Biochemical engineering.
Operations Research, Management Science.
Data Mining and Knowledge Discovery.
Health Informatics.
Biochemical Engineering.


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