Evolving Connectionist Systems: Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines
Machine learning in DNA microarray analysis for cancer classification
APBC '03 Proceedings of the First Asia-Pacific bioinformatics conference on Bioinformatics 2003 - Volume 19
A novel feature selection method to improve classification of gene expression data
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Cluster ensemble and its applications in gene expression analysis
APBC '04 Proceedings of the second conference on Asia-Pacific bioinformatics - Volume 29
Gene Cluster Algorithm Based on Most Similarity Tree
HPCASIA '05 Proceedings of the Eighth International Conference on High-Performance Computing in Asia-Pacific Region
A Novel EPA-KNN Gene Classification Algorithm
ISNN '07 Proceedings of the 4th international symposium on Neural Networks: Part II--Advances in Neural Networks
A novel relative space based gene feature extraction and cancer recognition
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
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The development of DNA array technology makes it feasible to cancer detection with DNA array expression data. However, the research is usually plagued with the problem of “curse of dimensionality”, and the capability of discrimination is weakened seriously by the noise and the redundancy that are abundant in these datasets. This paper proposes a hybrid gene selection method for cancer detection based on clustering of most similarity tree (CMST). By this method, a number of non-redundant clusters and the most discriminating gene from each cluster can be acquired. These discriminating genes are then used for training of a perceptron that produces a very efficient classification. In CMST, the Gap statistic is used to determine the optimal similarity measure λ and the number of clusters. And a gene selection method with optimal self-adaptive CMST(OS-CMST) for cancer detection is presented. The experiments show that the gene pattern pre-processing based on CMST not only reduces the dimensionality of the attributes significantly but also improves the classification rate effectively in cancer detection. And the selection scheme based on OS-CMST can acquire the top most discriminating genes.