Second Harmonic Generation imaging of collagen fibrillogenesis
2013 Conference on Lasers & Electro-Optics Europe & International Quantum Electronics Conference CLEO EUROPE/IQEC. 2013-05-01; :
DOI: 10.1109/cleoe-iqec.2013.6801528
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Liang C, Li Y, Luo J.
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.
DOI: 10.1109/TCBB.2015.2462370
PMID: 27295638 [Indexed for MEDLINE]