Journal ArticleDOI
A novel method to detect functional microRNA regulatory modules by bicliques merging
Cheng Liang,Yue Li,Jiawei Luo +2 more
TLDR
An effective method called BiCliques Merging (BCM) is developed to predict MRMs based on bicliques merging and it is shown that the modules identified by this method are more densely connected and functionally enriched.Abstract:
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 .read more
Citations
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Journal ArticleDOI
A graph regularized non-negative matrix factorization method for identifying microRNA-disease associations.
TL;DR: A new method with graph regularized non-negative matrix factorization in heterogeneous omics data, called GRNMF, to discover potential associations between miRNAs and diseases with higher accuracy compared with other recent approaches is proposed.
Journal ArticleDOI
Non-coding RNAs: long non-coding RNAs and microRNAs in endocrine-related cancers
TL;DR: An overview on the current understanding of the regulation and function of selected lncRNAs and miRNAs, and their interaction, in endocrine-related cancers: breast, prostate, endometrial and thyroid is provided.
Journal ArticleDOI
The recent development of metal oxide heterostructures based gas sensor, their future opportunities and challenges: A review
TL;DR: In this paper, various fabrication methods to synthesize metal-oxide-based heterostructure with different morphologies and dimensions have been reviewed and different types of mechanisms that improved the gas sensing performance, have been discussed.
Journal ArticleDOI
Patterning of human epidermal stem cells on undulating elastomer substrates reflects differences in cell stiffness.
Seyedeh Atefeh Mobasseri,Sebastiaan Zijl,Vasiliki Salameti,Gernot Walko,Andrew Stannard,Sergi Garcia-Manyes,Fiona M. Watt +6 more
TL;DR: In this article, it was shown that epidermal stem cell patterning is determined by mechanical forces exerted at intercellular junctions in response to the slope of the undulations.
Journal ArticleDOI
Detecting Sensitive Mobility Features for Parkinson's Disease Stages Via Machine Learning.
Anat Mirelman,Anat Mirelman,Mor Ben Or Frank,Michal Melamed,Lena Granovsky,Alice Nieuwboer,Lynn Rochester,Silvia Del Din,Laura Avanzino,Elisa Pelosin,Bastiaan R. Bloem,Ugo Della Croce,Andrea Cereatti,Andrea Cereatti,Paolo Bonato,Richard Camicioli,Theresa Ellis,Jamie L. Hamilton,Chris J. Hass,Quincy J. Almeida,Maidan Inbal,Maidan Inbal,Avner Thaler,Avner Thaler,Julia C Shirvan,Jesse M. Cedarbaum,Nir Giladi,Nir Giladi,Jeffrey M. Hausdorff +28 more
TL;DR: In this paper, the authors identify the gait and mobility measures that are most sensitive and reflective of Parkinson's motor stages and determine the optimal sensor location in each disease stage by applying machine learning to multiple wearable-derived features.
References
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