Wavelet transformation and cluster ensemble for gene expression analysis
International Journal of Bioinformatics Research and Applications
An Evolutionary Hierarchical Clustering Method with a Visual Validation Tool
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence
Mining gene expression patterns for the discovery of overlapping clusters
EvoBIO'08 Proceedings of the 6th European conference on Evolutionary computation, machine learning and data mining in bioinformatics
Towards a memetic feature selection paradigm
IEEE Computational Intelligence Magazine
A novel probabilistic encoding for EAs applied to biclustering of microarray data
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Towards ad-hoc rule semantics for gene expression data
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
WILF'05 Proceedings of the 6th international conference on Fuzzy Logic and Applications
Unifying Framework for Rule Semantics: Application to Gene Expression Data
Fundamenta Informaticae - Special issue ISMIS'05
RFS: Efficient feature selection method based on R-value
Computers in Biology and Medicine
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A Practical Approach to Microarray Data Analysis is for all life scientists, statisticians, computer experts, technology developers, managers, and other professionals tasked with developing, deploying, and using microarray technology including the necessary computational infrastructure and analytical tools. The book addresses the requirement of scientists and researchers to gain a basic understanding of microarray analysis methodologies and tools. It is intended for students, teachers, researchers, and research managers who want to understand the state of the art and of the presented methodologies and the areas in which gaps in our knowledge demand further research and development. The book is designed to be used by the practicing professional tasked with the design and analysis of microarray experiments or as a text for a senior undergraduate- or graduate level course in analytical genetics, biology, bioinformatics, computational biology, statistics and data mining, or applied computer science. Key topics covered include: -Format of result from data analysis, analytical modeling/experimentation; -Validation of analytical results; -Data analysis/Modeling task; -Analysis/modeling tools; -Scientific questions, goals, and tasks; -Application; -Data analysis methods; -Criteria for assessing analysis methodologies, models, and tools.