Inference of epigenetic subnetworks by Bayesian regression with the incorporation of prior information.

Changes in gene expression have been thought to play a crucial role in various types of cancer. With the advance of high-throughput experimental techniques, many genome-wide studies are underway to analyze underlying mechanisms that may drive the changes in gene expression. It has been observed that the change could arise from altered DNA methylation.

Expression-based analyses indicate a central role for hypoxia in driving tumor plasticity through microenvironment remodeling and chromosomal instability.

Can transcriptomic alterations drive the evolution of tumors? We asked if changes in gene expression found in all patients arise earlier in tumor development and can be relevant to tumor progression. Our analyses of non-mutated genes from the non-amplified regions of the genome of 158 triple-negative breast cancer (TNBC) cases identified 219 exclusively expression-altered (EEA) genes that may play important role in TNBC. Phylogenetic analyses of these genes predict a "punctuated burst" of multiple gene upregulation events occurring at early stages of tumor development, followed by minimal subsequent changes later in tumor progression.

A novel feature extraction scheme for prediction of protein-protein interaction sites.

Identifying protein-protein interaction (PPI) sites plays an important and challenging role in some topics of biology. Although many methods have been proposed, this problem is still far away to be solved. Here, a feature selection approach with an 11-sliding window and random forest algorithm is proposed, which is called DX-RF.