Random walks on mutual microRNA-target gene interaction network improve the prediction of disease-associated microRNAs

Background: MicroRNAs (miRNAs) have been shown to play an important role in pathological initiation, progression and maintenance. Because identification in the laboratory of disease-related miRNAs is not straightforward, numerous network-based methods have been developed to predict novel miRNAs in silico. Homogeneous networks (in which every node is a miRNA) based on the targets shared between miRNAs have been widely used to predict their role in disease phenotypes. Although such homogeneous networks can predict potential disease-associated miRNAs, they do not consider the roles of the target genes of the miRNAs. Here, we introduce a novel method based on a heterogeneous network that not only considers miRNAs but also the corresponding target genes in the network model. Results: Instead of constructing homogeneous miRNA networks, we built heterogeneous miRNA networks consisting of both miRNAs and their target genes, using databases of known miRNA-target gene interactions. In addition, as recent studies demonstrated reciprocal regulatory relations between miRNAs and their target genes, we considered these heterogeneous miRNA networks to be undirected, assuming mutual miRNA-target interactions. Next, we introduced a novel method (RWRMTN) operating on these mutual heterogeneous miRNA networks to rank candidate disease-related miRNAs using a random walk with restart (RWR) based algorithm. Using both known disease-associated miRNAs and their target genes as seed nodes, the method can identify additional miRNAs involved in the disease phenotype. Experiments indicated that RWRMTN outperformed two existing state-of-the-art methods: RWRMDA, a network-based method that also uses a RWR on homogeneous (rather than heterogeneous) miRNA networks, and RLSMDA, a machine learning-based method. Interestingly, we could relate this performance gain to the emergence of "disease modules" in the heterogeneous miRNA networks used as input for the algorithm. Moreover, we could demonstrate that RWRMTN is stable, performing well when using both experimentally validated and predicted miRNA-target gene interaction data for network construction. Finally, using RWRMTN, we identified 76 novel miRNAs associated with 23 disease phenotypes which were present in a recent database of known disease-miRNA associations. Conclusions: Summarizing, using random walks on mutual miRNA-target networks improves the prediction of novel disease-associated miRNAs because of the existence of "disease modules" in these networks

Booms and Busts: the Burstiness of Star Formation in Nearby Dwarf Galaxies

In this review I summarise recent advances in our understanding of the importance of starburst events to the evolutionary histories of nearby galaxies. Ongoing bursts are easily diagnosed in emission-line surveys, but assessing the timing and intensity of fossil bursts requires more effort, usually demanding color-magnitude diagrams or spectroscopy of individual stars. For ages older than ~1 Gyr, this type of observation is currently limited to the Local Group and its immediate surroundings. However, if the Local Volume is representative of the Universe as a whole, then studies of the age and metallicity distributions of star clusters and resolved stellar populations should give statistical clues as to the frequency and importance of bursts to the histories of galaxies in general. Based on starburst statistics in the literature and synthetic colour-magnitude diagram studies of Local Group galaxies, I attempt to distinguish between systemic starbursts that strongly impact galaxy evolution and stochastic bursts that can appear impressive but are ultimately of little significance on gigayear timescales. As a specific case, it appears as though IC 10, the only starburst galaxy in the Local Group, falls into the latter category and is not fundamentally different from other nearby dwarf irregular galaxies.Comment: Accepted by the Publications of the Astronomical Society of Australia (PASA). Summary of a review talk given at the Southern Cross Astrophysics Conference on "Galaxy Metabolism" held in Sydney, 22-26 June 2009. 9 pages, 2 figure