Neural Networks
Instance-Based Learning Algorithms
Machine Learning
Elements of information theory
Elements of information theory
C4.5: programs for machine learning
C4.5: programs for machine learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Identifying Simple Discriminatory Gene Vectors with an Information Theory Approach
CSB '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference
Data mining in bioinformatics using Weka
Bioinformatics
A Mathematical Theory of Communication
A Mathematical Theory of Communication
An analysis of Bayesian classifiers
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Exploring features and classifiers to classify microRNA expression profiles of human cancer
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: models and applications - Volume Part II
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Some non-coding small RNAs, known as microRNAs (miRNAs), have been shown to play important roles in gene regulation and various biological processes. The abnormal expression of some specific miRNA genes often results in the development of cancer. In this paper, we find discriminatory miRNA patterns for cancer classification from miRNA expression profiles. The experimental results show that the expression patterns from a small set of miRNAs are very accurate in prediction. Further, the experimental results also suggest that the expression patterns of these informative miRNAs are conserved in different vertebrates during the evolution process.