Abstract
Genes and their expression regulation are among the key factors in the comprehension of the genesis and development of complex diseases. In this context, microRNAs (miRNAs) are post-transcriptional regulators that play an important role in gene expression since they are frequently deregulated in pathologies like cardiovascular disease and cancer. In vitro validation of miRNA - targets regulation is often too expensive and time consuming to be carried out for every possible alternative. As a result, a tool able to provide some criteria to prioritize trials is becoming a pressing need. Moreover, before planning in vitro experiments, the scientist needs to evaluate the miRNA-target genes interaction network. In this paper we describe the miRable method whose purpose is to identify new potentially relevant genes and their interaction networks associate to a specific pathology. To achieve this goal miRable follows a system biology approach integrating together general-purpose medical knowledge (literature, Protein-Protein Interaction networks, prediction tools) and pathology specific data (gene expression data). A case study on Prostate Cancer has shown that miRable is able to: 1) find new potential miRNA-targets pairs, 2) highlight novel genes potentially involved in a disease but never or little studied before, 3) reconstruct all possible regulatory subnetworks starting from the literature to expand the knowledge on the regulation of miRNA regulatory mechanisms.
Highlights
Nowadays a huge amount of biological data is available to scientists to be used to dissect the complexity of a disease
In this paper we describe miRable, a new method that takes into account the disease-specific context, mRNA—miRNA expression data and protein-protein interaction networks to provide a landscape of the complex miRNA-gene regulatory networks that are at the root of a specific pathology
Starting from the literature information our method applies different constraints and filters to build the extended regulatory network, and exploits it to find all the regulatory subnetworks involved in a disease
Summary
Nowadays a huge amount of biological data (e.g. gene and protein expression data) is available to scientists to be used to dissect the complexity of a disease. Extracting useful information from biological databases is a complex task, it has to be understood and mined searching for the sparkling gems This is a though job to do without the help of tools able to identify the most promising options. Methods based on the integration of mRNA and miRNA expressions can improve the prediction accuracy, even if they do not take into account the importance of each gene in relation to its disease-specific regulatory network. In this paper we describe miRable, a new method that takes into account the disease-specific context, mRNA—miRNA expression data and protein-protein interaction networks to provide a landscape of the complex miRNA-gene regulatory networks that are at the root of a specific pathology. We tested our method on Prostate Cancer (PCa) discovering a promising gene and two miRNAs still unstudied in conjunction with this pathology
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