Data Mining in Biomedicine [electronic resource] /
Pardalos, Panos M.editor.(orcid)0000-0003-2824-101Xhttps://orcid.org/0000-0003-2824-101Xedt
Boginski, Vladimir L.editor.
Vazacopoulos, Alkis.editor.
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 practitio
Description based upon print version of record.
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 practitio
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.
Medicine.
Applied mathematics.
Engineering mathematics.
Operations research.
Management science.
Biomedical engineering.
Statistics .
Biomedicine, general.
Applications of Mathematics.
Operations Research, Management Science.
Biomedical Engineering and Bioengineering.
Statistics for Life Sciences, Medicine, Health Sciences.
Springer optimization and its applications