Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Attribute Clustering for Grouping, Selection, and Classification of Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Interpretation of gene expression microarray experiments
Proceedings of the 2007 conference on Emerging Artificial Intelligence Applications in Computer Engineering: Real Word AI Systems with Applications in eHealth, HCI, Information Retrieval and Pervasive Technologies
A Framework for Multi-class Learning in Micro-array Data Analysis
AIME '09 Proceedings of the 12th Conference on Artificial Intelligence in Medicine: Artificial Intelligence in Medicine
Journal of Artificial Evolution and Applications - Special issue on artificial evolution methods in the biological and biomedical sciences
GUEST EDITORIAL: Computational intelligence in solving bioinformatics problems
Artificial Intelligence in Medicine
Capturing heuristics and intelligent methods for improving micro-array data classification
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Mining of MicroRNA expression data—a rough set approach
RSKT'06 Proceedings of the First international conference on Rough Sets and Knowledge Technology
Gene selection based on mutual information for the classification of multi-class cancer
ICIC'06 Proceedings of the 2006 international conference on Computational Intelligence and Bioinformatics - Volume Part III
A quantitative evaluation of techniques for detection of abnormal change events in blogs.
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
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New genomic and proteomic technologies provide measurements of thousands of features for each case. This provides a context for enhanced discovery and false discovery. Most statistical and machine learning procedures were not developed for the pn setting and the literature of DNA microarray studies contains many examples of mis-use of analytic and computatinal methods such a cross-validation. This paper highlights some of key aspects of pn problems for identifying informative features and developing accurate classifiers.