Browsing KEDRI - the Knowledge Engineering and Discovery Research Institute by Author "Chan, Z."
Now showing items 1-4 of 4
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A two-stage methodology for gene regulatory network extraction from time-course gene expression data
Chan, Z.; Kasabov, N; Collins, L. (IEEE, 2004)The discovery of gene regulatory networks (GRN) from time-course gene expression data (gene trajectory data) is useful for (1) identifying important genes in relation to a disease or a biological function; (2) gaining an ... -
Efficient global clustering using the greedy elimination method
Chan, Z.; Kasabov, N (IEEE, 2004)A novel global clustering method called the greedy elimination method is presented. Experiments show that the proposed method scores significantly lower clustering errors than the standard K-means over two benchmark and ... -
Fast Neural Network Ensemble Learning via Negative-Correlation Data Correction
Chan, Z.; Kasabov, N (IEEE, 2005)This letter proposes a new negative correlation (NC) learning method that is both easy to implement and has the advantages that: 1) it requires much lesser communication overhead than the standard NC method and 2) it is ... -
Gene trajectory clustering with a hybrid genetic algorithm and expectation maximization method
Chan, Z.; Kasabov, N (IEEE, 2004)Clustering time course gene expression data (gene trajectories) is an important step towards solving the complex problem of gene regulatory network (GRN) modeling and discovery as it significantly reduces the dimensionality ...