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978-0-387-69319-4 |
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|a 9780387693194
|9 978-0-387-69319-4
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|a 10.1007/978-0-387-69319-4
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|a 610.72
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|a Data Mining in Biomedicine
|h [electronic resource] /
|c edited by Panos M. Pardalos, Vladimir L. Boginski, Alkis Vazacopoulos.
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|a 1st ed. 2007.
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|a New York, NY :
|b Springer US :
|b Imprint: Springer,
|c 2007.
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|a XVIII, 580 p.
|b online resource.
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|a text
|b txt
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|a Springer Optimization and Its Applications,
|x 1931-6836 ;
|v 7
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| 505 |
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|a Recent Methodological Developments for Data Mining Problems in Biomedicine -- Pattern-Based Discriminants in the Logical Analysis of Data -- Exploring Microarray Data with Correspondence Analysis -- An Ensemble Method of Discovering Sample Classes Using Gene Expression Profiling -- CpG Island Identification with Higher Order and Variable Order Markov Models -- Data Mining Algorithms for Virtual Screening of Bioactive Compounds -- Sparse Component Analysis: a New Tool for Data Mining -- Data Mining Via Entropy and Graph Clustering -- Molecular Biology and Pooling Design -- An Optimization Approach to Identify the Relationship between Features and Output of a Multi-label Classifier -- Classifying Noisy and Incomplete Medical Data by a Differential Latent Semantic Indexing Approach -- Ontology Search and Text Mining of MEDLINE Database -- Data Mining Techniques in Disease Diagnosis -- Logical Analysis of Computed Tomography Data to Differentiate Entities of Idiopathic Interstitial Pneumonias -- Diagnosis of Alport Syndrome by Pattern Recognition Techniques -- Clinical Analysis of the Diagnostic Classification of Geriatric Disorders -- Data Mining Studies in Genomics and Proteomics -- A Hybrid Knowledge Based-Clustering Multi-Class SVM Approach for Genes Expression Analysis -- Mathematical Programming Formulations for Problems in Genomics and Proteomics -- Inferring the Origin of the Genetic Code -- Deciphering the Structures of Genomic DNA Sequences Using Recurrence Time Statistics -- Clustering Proteomics Data Using Bayesian Principal Component Analysis -- Bioinformatics for Traumatic Brain Injury: Proteomic Data Mining -- Characterization and Prediction of Protein Structure -- Computational Methods for Protein Fold Prediction: an Ab-initio Topological Approach -- A Topological Characterization of Protein Structure -- Applications of Data Mining Techniques to Brain Dynamics Studies -- Data Mining in EEG: Application to Epileptic Brain Disorders -- Information Flow in Coupled Nonlinear Systems: Application to the Epileptic Human Brain -- Reconstruction of Epileptic Brain Dynamics Using Data Mining Techniques -- Automated Seizure Prediction Algorithm and its Statistical Assessment: A Report from Ten Patients -- Seizure Predictability in an Experimental Model of Epilepsy -- Network-Based Techniques in EEG Data Analysis and Epileptic Brain Modeling.
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|a Medicine-Research.
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|a Biology-Research.
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|a Mathematics.
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|a Operations research.
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|a Management science.
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|a Biomedical engineering.
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|a Biometry.
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|a Biomedical Research.
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|a Applications of Mathematics.
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| 650 |
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|a Operations Research, Management Science .
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| 650 |
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|a Biomedical Engineering and Bioengineering.
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| 650 |
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|a Biostatistics.
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| 700 |
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|a Pardalos, Panos M.
|e editor.
|0 (orcid)0000-0003-2824-101X
|1 https://orcid.org/0000-0003-2824-101X
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Boginski, Vladimir L.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 700 |
1 |
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|a Vazacopoulos, Alkis.
|e editor.
|4 edt
|4 http://id.loc.gov/vocabulary/relators/edt
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| 710 |
2 |
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|a SpringerLink (Online service)
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|t Springer Nature eBook
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|i Printed edition:
|z 9780387565101
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|i Printed edition:
|z 9781441943439
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| 776 |
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|i Printed edition:
|z 9780387693187
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|a Springer Optimization and Its Applications,
|x 1931-6836 ;
|v 7
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4 |
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|u https://doi.uam.elogim.com/10.1007/978-0-387-69319-4
|z Texto Completo
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