IEEE/ACM Trans Comput Biol Bioinform 2016 May-Jun;13(3):549-56
Unlabelled: MicroRNAs (miRNAs) are post-transcriptional regulators that repress the expression of their targets. They are known to work cooperatively with genes and play important roles in numerous cellular processes. Identification of miRNA regulatory modules (MRMs) would aid deciphering the combinatorial effects derived from the many-to-many regulatory relationships in complex cellular systems. Here, we develop an effective method called BiCliques Merging (BCM) to predict MRMs based on bicliques merging. By integrating the miRNA/mRNA expression profiles from The Cancer Genome Atlas (TCGA) with the computational target predictions, we construct a weighted miRNA regulatory network for module discovery. The maximal bicliques detected in the network are statistically evaluated and filtered accordingly. We then employed a greedy-based strategy to iteratively merge the remaining bicliques according to their overlaps together with edge weights and the gene-gene interactions. Comparing with existing methods on two cancer datasets from TCGA, we showed that the modules identified by our method are more densely connected and functionally enriched. Moreover, our predicted modules are more enriched for miRNA families and the miRNA-mRNA pairs within the modules are more negatively correlated. Finally, several potential prognostic modules are revealed by Kaplan-Meier survival analysis and breast cancer subtype analysis.
Availability: BCM is implemented in Java and available for download in the supplementary materials, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/ TCBB.2015.2462370.
Abstract Video
Parallel reinforced learning: an all-in-one AI solution
We have submitted your request - we will update you on status within the next 24 hours.
Sign up for further access to Scientific Publications and Authors!
PubFacts Points
What are PubFacts Points?
PubFacts points are rewards to PubFacts members, which allow you to better promote your profile and articles throughout PubFacts.com
How do I earn PubFacts Points?
Each member is given 50 PubFacts points upon signing up. You can earn additional points by completing 100% of your profile, creating and participating in discussions, and sharing other members research.
What can I do with PubFacts Points?
Currently, you can use PubFacts Points to promote and increase readership of your articles.