Data Mining in Biomedicine (Hardcover)

購買商店

Data Mining in Biomedicine (Hardcover)

$4,980.00

馬上購買

內容描述

Description

This volume presents an extensive collection of chapters covering various
aspects of the exciting and important research area of data mining techniques
in biomedicine. The topics include: - new approaches for the analysis of
biomedical data, - applications of data mining techniques to real-life
problems in medical practice, - comprehensive reviews of recent trends in the
field.
The book addresses the problems arising in fundamental areas of biomedical
research, such as genomics, proteomics, protein characterization, and
neuroscience.
This volume would be of interest to scientists and practitioners working in
the field of biomedicine, as well as related areas of engineering,
mathematics, and computer science. It can also be helpful to graduate students
and young researchers looking for new exciting directions in their work. Since
each chapter can be read independently, readers interested in specific
problems and applications may find the material of certain chapters
useful.
Table of Contents

Preface . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. ixList of Contributors . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . xiPart I Recent Methodological
Developments for Data MiningProblems in BiomedicinePattern-Based
Discriminants in the Logical Analysis of DataSorin Alexe, Peter L. Hammer
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3Exploring Microarray Data with Correspondence AnalysisStanislav
Busygin, Panos M. Pardalos . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . 25An Ensemble Method of Discovering Sample Classes UsingGene
Expression ProfilingDechang Chen, Zhe Zhang, Zhenqiu Liu, Xiuzhen Cheng .
. . . . . . . . . . . . . 39CpG Island Identification with Higher Order
and VariableOrder Markov ModelsZhenqiu Liu, Dechang Chen, Xue-wen Chen
. . . . . . . . . . . . . . . . . . . . . . . . . 47Data Mining Algorithms
for Virtual Screening of BioactiveCompoundsMukund Deshpande, Michihiro
Kuramochi, George Karypis . . . . . . . . . . . . 59Sparse Component
Analysis: a New Tool for Data MiningPando Georgiev, Fabian Theis, Andrzej
Cichocki, Hovagim Bakardjian . . 91Data Mining Via Entropy and Graph
ClusteringAnthony Okafor, Panos Pardalos, Michelle Ragle . . . . . . . . .
. . . . . . . . . . . 117
Molecular Biology and Pooling
DesignWeili Wu, Yingshu Li, Chih-hao Huang, Ding-Zhu Du . . . . . . . . .
. . . . . . . 133An Optimization Approach to Identify the
Relationshipbetween Features and Output of a Multi-label
ClassifierMusa Mammadov, Alex Rubinov, John Yearwood . . . . . . . . . . .
. . . . . . . . . . 141Classifying Noisy and Incomplete Medical Data by
aDifferential Latent Semantic Indexing ApproachLiang Chen, Jia Zeng,
Jian Pei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. 169Ontology Search and Text Mining of MEDLINE DatabaseHyunki Kim,
Su-Shing Chen. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . 177Part II Data Mining Techniques in Disease
DiagnosisLogical Analysis of Computed Tomography Data toDifferentiate
Entities of Idiopathic Interstitial PneumoniasM.W. Brauner, N. Brauner,
P.L. Hammer, I. Lozina, D. Valeyre . . . . . . 193Diagnosis of Alport
Syndrome by Pattern RecognitionTechniquesGiacomo Patrizi, Gabriella
Addonisio, Costas Giannakakis, AndreaOnetti Muda, Gregorio Patrizi, Tullio
Faraggiana . . . . . . . . . . . . . . . . . . . . 209Clinical Analysis of
the Diagnostic Classification of GeriatricDisordersGiacomo Patrizi,
Gregorio Patrizi, Luigi Di Cioccio, Claudia Bauco . . . . 231Part III Data
Mining Studies in Genomics and ProteomicsA Hybrid Knowledge
Based-Clustering Multi-Class SVMApproach for Genes Expression
AnalysisBudi Santosa, Tyrrell Conway, Theodore Trafalis . . . . . . . . .
. . . . . . . . . . . 261Mathematical Programming Formulations for
Problems inGenomics and ProteomicsCl’audio N. Meneses, Carlos A.S.
Oliveira, Panos M. Pardalos . . . . . . . . . 275Inferring the Origin of
the Genetic CodeMaria Luisa Chiusano, Luigi Frusciante, Gerardo Toraldo. .
. . . . . . . . . . . 291Deciphering the Structures of Genomic DNA
SequencesUsing Recurrence Time StatisticsJian-Bo Gao, Yinhe Cao,
Wen-wen Tung . . . . . . . . . . . . . . . . . . . . . . . . . . . 321
Clustering Proteomics Data Using
Bayesian PrincipalComponent AnalysisHalima Bensmail, O. John Semmes,
Abdelali Haoudi . . . . . . . . . . . . . . . . . 339Bioinformatics for
Traumatic Brain Injury: Proteomic DataMiningSu-Shing Chen, William E.
Haskins, Andrew K. Ottens, Ronald L.Hayes, Nancy Denslow, Kevin K.W. Wang
. . . . . . . . . . . . . . . . . . . . . . . . . . 363Part IV
Characterization and Prediction of Protein StructureComputational Methods
for Protein Fold Prediction: anAb-initio Topological ApproachG. Ceci,
A. Mucherino, M. D’Apuzzo, D. Di Serafino, S. Costantini,A. Facchiano, G.
Colonna. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . 391A Topological Characterization of Protein StructureBala
Krishnamoorthy, Scott Provan, Alexander Tropsha . . . . . . . . . . . . . . .
431Part V Applications of Data Mining Techniques to Brain
DynamicsStudiesData Mining in EEG: Application to Epileptic
BrainDisordersW. Chaovalitwongse, P.M. Pardalos, L.D. Iasemidis,
W.Suharitdamrong, D.-S. Shiau, L.K. Dance, O.A. Prokopyev,
V.L.Boginski, P.R. Carney, J.C. Sackellares . . . . . . . . . . . . . . .
. . . . . . . . . . . . . 459Information Flow in Coupled Nonlinear
Systems: Applicationto the Epileptic Human BrainS. Sabesan, K.
Narayanan, A. Prasad, L. D. Iasemidis, A. Spanias, K.Tsakalis . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . 483Reconstruction of Epileptic Brain Dynamics Using
DataMining TechniquesPanos M. Pardalos, Vitaliy A. Yatsenko . . . . .
. . . . . . . . . . . . . . . . . . . . . . . 505Automated Seizure
Prediction Algorithm and its StatisticalAssessment: A Report from Ten
PatientsD.-S. Shiau, L.D. Iasemidis, M.C.K. Yang, P.M. Pardalos,
P.R.Carney, L.K. Dance, W. Chaovalitwongse, J.C. Sackellares . . . . . . .
. . . . 517Seizure Predictability in an Experimental Model of
EpilepsyS.P. Nair, D.-S. Shiau, L.D. Iasemidis, W.M. Norman, P.M.
Pardalos,J.C. Sackellares, P.R. Carney . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . . . . . . . . . 535
Network-Based Techniques in EEG Data
Analysis andEpileptic Brain ModelingOleg A. Prokopyev, Vladimir L.
Boginski, Wanpracha Chaovalitwongse,Panos M. Pardalos, J. Chris
Sackellares, Paul R. Carney . . . . . . . . . . . . 559Index . . . . . . .
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . . .
575