Document Detail


Discovery of miR-mRNA interactions via simultaneous Bayesian inference of gene networks and clusters using sequence-based predictions and expression data.
MedLine Citation:
PMID:  23846182     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
MicroRNAs (miRs) are known to interfere with mRNA expression, and much work has been put into predicting and inferring miR-mRNA interactions. Both sequence-based interaction predictions as well as interaction inference based on expression data have been proven somewhat successful; furthermore, models that combine the two methods have had even more success. In this paper, I further refine and enrich the methods of miRmRNA interaction discovery by integrating a Bayesian clustering algorithm into a model of prediction-enhanced miR-mRNA target inference, creating an algorithm called PEACOAT, which is written in the R language. I show that PEACOAT improves the inference of miR-mRNA target interactions using both simulated data and a data set of microarrays from samples of multiple myeloma patients. In simulated networks of 25 miRs and mRNAs, our methods using clustering can improve inference in roughly two-thirds of cases, and in the multiple myeloma data set, KEGG pathway enrichment was found to be more significant with clustering than without. Our findings are consistent with previous work in clustering of non-miR genetic networks and indicate that there could be a significant advantage to clustering of miR and mRNA expression data as a part of interaction inference.
Authors:
Brian Godsey
Publication Detail:
Type:  Journal Article     Date:  2013-07-10
Journal Detail:
Title:  Journal of integrative bioinformatics     Volume:  10     ISSN:  1613-4516     ISO Abbreviation:  J Integr Bioinform     Publication Date:  2013  
Date Detail:
Created Date:  2013-07-12     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101503361     Medline TA:  J Integr Bioinform     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  227     Citation Subset:  IM    
Affiliation:
Department of Statistics and Probability Theory, Vienna University of Technology, 1040 Vienna, Austria.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Synthesis, characterization, and DNA binding, photocleavage, cytotoxicity, cellular uptake, apoptosi...
Next Document:  The Bark Beetle Holobiont: Why Microbes Matter.