Data Mining for Biomarker Discovery

This book applies data mining techniques to the discovery and analysis of new biomarkers. Presenting new results, models and algorithms, coverage focuses on biomarker data integration, information retrieval methods and statistical machine learning techniques. Top page

Complete description

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. Top page

General info

Publisher & Imprint: Springer-Verlag New York Inc.

City: New York, NY

Pages: 260

More info: height 235 mm width 155 mm weight 555 gr thickness 23 mm

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Age recommended: Professional and scholarly

Subject Indexing & Classification Dewey:(DC23) 610.28

Departments: Medical research;

Record updated at: 03 May, 2013 time: 03:53

Summary Data Mining for Biomarker Discovery 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 Interation 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. Perez, F. Henriques, R. Santaolaya, O. Fragoso, A. Mexicano). Top page

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