Data Integration in Functional Analysis of MicroRNAs

Akkaya, Mahinur
The discovery of microRNAs (miRNAs), about a decade ago, has completely changed our understanding of the complexity of gene regulatory networks. It has already been shown that they are abundantly found in many organisms and can regulate hundreds of genes in post-transcriptional level. To elucidate the individual or co-operative effects of miRNAs, it is required to place them in the overall network of gene regulation and link them to other pathways and systems-level processes. One key step in this effort is predicting targets of individual miRNAs. Although current tools are helpful in predicting miRNA-mRNA binding to a considerable extent, they are not able to model many-to-many relationships between miRNAs and their targets using solely sequence information. Therefore, other types of information sources have been employed for better prediction of these functional relationships. This report focuses on the state-of-the-art solutions and current challenges on mining miRNA-related data to discover the systems-level role of miRNAs, with an emphasis on the integration of different information sources. We aim to provide new insights for fusion of different types of biochemical and experimental information sources which may facilitate functional analysis of miRNAs.


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Objective: Formulate the induction and control of gene regulatory networks (GRNs) from gene expression data using Partially Observable Markov Decision Processes (POMDPs).
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Inference of gene regulatory or signaling networks from perturbation experiments and gene expression assays is one of the challenging problems in bioinformatics. Recently, the inference problem has been formulated as a reference network editing problem and it has been show that finding the minimum number of edit operations on a reference network in order to comply with perturbation experiments is an NP-complete problem. In this dissertation, we propose linear programming based solutions for reconstruction o...
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Citation Formats
H. OĞUL and M. Akkaya, “Data Integration in Functional Analysis of MicroRNAs,” CURRENT BIOINFORMATICS, pp. 462–472, 2011, Accessed: 00, 2020. [Online]. Available: