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Data Mining for Biomarker Discovery

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Cover of 'Data Mining for Biomarker Discovery'

Table of Contents

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    Book Overview
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    Chapter 1 Data Mining Strategies Applied in Brain Injury Models
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    Chapter 2 Application of Decomposition Methods in the Filtering of Event-Related Potentials
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    Chapter 3 EEG Features as Biomarkers for Discrimination of Preictal States
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    Chapter 4 Using Relative Power Asymmetry as a Biomarker for Classifying Psychogenic Nonepileptic Seizure and Complex Partial Seizure Patients
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    Chapter 5 Classification of Tree and Network Topology Structures in Medical Images
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    Chapter 6 A Framework for Multimodal Imaging Biomarker Extraction with Application to Brain MRI
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    Chapter 7 A Statistical Diagnostic Decision Support Tool Using Magnetic Resonance Spectroscopy Data
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    Chapter 8 Data Mining for Cancer Biomarkers with Raman Spectroscopy
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    Chapter 9 Nonlinear Recognition Methods for Oncological Pathologies
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    Chapter 10 Studying Connectivity Properties in Human Protein–Protein Interaction Network in Cancer Pathway
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    Chapter 11 Modelling of Oral Cancer Progression Using Dynamic Bayesian Networks
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    Chapter 12 Neuromuscular Alterations of Upper Airway Muscles in Patients with OSAS: Radiological and Histopathological Findings
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    Chapter 13 Data Mining System Applied to Population Databases for Studies on Lung Cancer
Attention for Chapter 1: Data Mining Strategies Applied in Brain Injury Models
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Citations

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Chapter title
Data Mining Strategies Applied in Brain Injury Models
Chapter number 1
Book title
Data Mining for Biomarker Discovery
Published by
Springer, Boston, MA, January 2012
DOI 10.1007/978-1-4614-2107-8_1
Book ISBNs
978-1-4614-2106-1, 978-1-4614-2107-8
Authors

Stefania Mondello, Firas Kobeissy, Isaac Fingers, Zhiqun Zhang, Ronald L. Hayes, Kevin K. W. Wang

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 1 Mendeley reader of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 1 100%

Demographic breakdown

Readers by professional status Count As %
Professor > Associate Professor 1 100%
Readers by discipline Count As %
Medicine and Dentistry 1 100%

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