Choosing the best set of variables in regression analysis using integer programming
Journal of Global Optimization
A Supervised Learning Technique and Its Applications to Computational Biology
Computational Intelligence Methods for Bioinformatics and Biostatistics
Multi-step methods for choosing the best set of variables in regression analysis
Computational Optimization and Applications
Supervised classification methods for mining cell differences as depicted by Raman spectroscopy
CIBB'10 Proceedings of the 7th international conference on Computational intelligence methods for bioinformatics and biostatistics
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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.