• Publications
  • Influence
The biological knowledge discovery by PCCF measure and PCA-F projection
From the analysis of simulated and experimental data sets, it is demonstrated that PCCF is more appropriate and reliable for analyzing gene expression data compared to other commonly used distances or similarity measures, and PCA-F is a good visualization technique for identifying clusters of P CCF. Expand
Analyzing the similarity of samples and genes by MG-PCC algorithm, t-SNE-SS and t-SNE-SG maps
From the analysis of cancer gene expression data sets, it is demonstrated that MG-PCC algorithm is able to put tumor and normal samples into their respective mini- groups, and t-SNE-SSP maps are able to display the relationships between mini-groups(or PCC clusters) clearly. Expand
Multiple‐cumulative probabilities used to cluster and visualize transcriptomes
Pearson's correlation coefficient of multiple‐cumulative probabilities (PCC‐MCP) of genes is used to define the similarity of gene expression behaviors to answer the challenge of the high‐dimensional MCPs. Expand
Mini-clusters with mean probabilities for identifying effective siRNAs
From the analysis of the siRNAs data, it is suggested that the mini-clusters algorithm with relative mean probabilities can provide new insights to the applications for distinguishing effective si RNAs from ineffective ones. Expand
Selecting highly effective siRNAs by their modified entropies with mini-clusters
A novel method of distinguishing effective siRNAAs is reported that combines the advantages of the score-based algorithms and the machine learning classification algorithms, where it use the modified entropies of siRNAs as feature indicator of si RNAs and split siRNA into many smaller effective or ineffective subclasses by a mini-clusters algorithm. Expand