Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Data Mining for Biomarker Discovery is designed to motivate collaboration and discussion among various disciplines and will be of interest to students and researchers in engineering, computer science, applied mathematics, medicine, and anyone interested in the interdisciplinary application of data mining techniques. Biomarker discovery is an important area of biomedical research that can lead to significant breakthroughs in disease analysis and targeted therapy. Moreover, the discovery and management of new biomarkers is a challenging and attractive problem in the emerging field of biomedical informatics. This volume is a collection of  state-of-the-art research from select participants of the “International Conference on Biomedical Data and Knowledge Mining: Towards Biomarker Discovery,” held July 7-9, 2010 in Chania, Greece. Contributions focus on biomarker data integration, information retrieval methods, and statistical machine learning techniques, all presented with new results, models, and algorithms.

Biochemical engineering. --- Biochemical markers -- Research -- Data processing. --- Data mining. --- Mathematics. --- Medical records -- Data processing. --- Information Storage and Retrieval --- Medical Informatics Applications --- Information Science --- Medical Informatics --- Data Mining --- Medicine --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Health & Biological Sciences --- Medical Research --- Operations Research --- Biochemical markers. --- Biologic markers --- Biological markers --- Biomarkers --- Markers, Biochemical --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Health informatics. --- Operations research. --- Management science. --- Operations Research, Management Science. --- Data Mining and Knowledge Discovery. --- Health Informatics. --- Biochemical Engineering. --- Database searching --- Biochemistry --- Indicators (Biology) --- Medical records --- Data processing. --- Bio-process engineering --- Bioprocess engineering --- Biotechnology --- Chemical engineering --- EHR systems --- EHR technology --- EHRs (Electronic health records) --- Electronic health records --- Electronic medical records --- EMR systems --- EMRs (Electronic medical records) --- Information storage and retrieval systems --- Medical care --- Clinical informatics --- Health informatics --- Medical information science --- Information science --- Quantitative business analysis --- Management --- Problem solving --- Operations research --- Statistical decision --- Operational analysis --- Operational research --- Industrial engineering --- Management science --- Research --- System theory --- Data processing

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The human brain is among the most complex systems known to mankind. Neuroscientists seek to understand brain function through detailed analysis of neuronal excitability and synaptic transmission. Only in the last few years has it become feasible to capture simultaneous responses from a large enough number of neurons to empirically test the theories of human brain function computationally. This book is comprised of state-of-the-art experiments and computational techniques that provide new insights and improve our understanding of the human brain. This volume includes contributions from diverse disciplines including electrical engineering, biomedical engineering, industrial engineering, and medicine, bridging a vital gap between the mathematical sciences and neuroscience research. Covering a wide range of research topics, this volume demonstrates how various methods from data mining, signal processing, optimization and cutting-edge medical techniques can be used to tackle the most challenging problems in modern neuroscience. The results presented in this book are of great interest and value to scientists, graduate students, researchers and medical practitioners interested in the most recent developments in computational neuroscience.

Mathematics --- Human biochemistry --- Neuropathology --- Planning (firm) --- Biotechnology --- Computer. Automation --- medische biochemie --- neurologie --- bio-engineering --- computers --- informatica --- mathematische modellen --- biotechnologie --- medische informatica --- wiskunde

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

The human brain is among the most complex systems known to mankind. Neuroscientists seek to understand brain function through detailed analysis of neuronal excitability and synaptic transmission. Only in the last few years has it become feasible to capture simultaneous responses from a large enough number of neurons to empirically test the theories of human brain function computationally. This book is comprised of state-of-the-art experiments and computational techniques that provide new insights and improve our understanding of the human brain. This volume includes contributions from diverse disciplines including electrical engineering, biomedical engineering, industrial engineering, and medicine, bridging a vital gap between the mathematical sciences and neuroscience research. Covering a wide range of research topics, this volume demonstrates how various methods from data mining, signal processing, optimization and cutting-edge medical techniques can be used to tackle the most challenging problems in modern neuroscience. The results presented in this book are of great interest and value to scientists, graduate students, researchers and medical practitioners interested in the most recent developments in computational neuroscience.

Brain -- Computer simulation. --- Brain -- physiology. --- Computer Simulation. --- Models, Neurological. --- Neural networks (Neurobiology). --- Neurosciences -- methods. --- Computational neuroscience --- Investigative Techniques --- Models, Biological --- Biological Science Disciplines --- Biology --- Models, Neurological --- Methods --- Neurosciences --- Computational Biology --- Natural Science Disciplines --- Models, Theoretical --- Analytical, Diagnostic and Therapeutic Techniques and Equipment --- Disciplines and Occupations --- Mathematics --- Human Anatomy & Physiology --- Neuroscience --- Mathematics - General --- Health & Biological Sciences --- Physical Sciences & Mathematics --- Computational neuroscience. --- Computational neurosciences --- Medicine. --- Neurosciences. --- Health informatics. --- Computer mathematics. --- Mathematical models. --- Biomedical engineering. --- Biomedicine. --- Health Informatics. --- Biomedical Engineering. --- Computational Mathematics and Numerical Analysis. --- Mathematical Modeling and Industrial Mathematics. --- Computational biology --- Medical records --- Computer science --- Biomedical Engineering and Bioengineering. --- Data processing. --- Mathematics. --- Computer mathematics --- Discrete mathematics --- Electronic data processing --- Clinical engineering --- Medical engineering --- Bioengineering --- Biophysics --- Engineering --- Medicine --- EHR systems --- EHR technology --- EHRs (Electronic health records) --- Electronic health records --- Electronic medical records --- EMR systems --- EMRs (Electronic medical records) --- Information storage and retrieval systems --- Neural sciences --- Neurological sciences --- Medical sciences --- Nervous system --- Medical care --- Models, Mathematical --- Simulation methods --- Clinical informatics --- Health informatics --- Medical information science --- Information science --- Data processing

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Data uncertainty is a concept closely related with most real life applications that involve data collection and interpretation. Examples can be found in data acquired with biomedical instruments or other experimental techniques. Integration of robust optimization in the existing data mining techniques aim to create new algorithms resilient to error and noise. This work encapsulates all the latest applications of robust optimization in data mining. This brief contains an overview of the rapidly growing field of robust data mining research field and presents  the most well known machine learning algorithms, their robust counterpart formulations and algorithms for attacking these problems. This brief will appeal to theoreticians and data miners working in this field.

Computer algorithms. --- Computer science. --- Data mining. --- Data mining --- Robust optimization --- Civil & Environmental Engineering --- Engineering & Applied Sciences --- Operations Research --- Computer Science --- Robust optimization. --- Optimization, Robust --- RO (Robust optimization) --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Mathematics. --- Software engineering. --- Mathematical optimization. --- Optimization. --- Data Mining and Knowledge Discovery. --- Software Engineering/Programming and Operating Systems. --- Database searching --- Mathematical optimization --- Computer software engineering --- Engineering --- Optimization (Mathematics) --- Optimization techniques --- Optimization theory --- Systems optimization --- Mathematical analysis --- Maxima and minima --- Operations research --- Simulation methods --- System analysis

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Biochemical engineering --- Information systems --- Computer. Automation --- bio-engineering --- biochemie --- automatisering --- medische informatica --- database management --- gegevensanalyse