MiR-Drug Relationships: Mining and discovering bi-domain dense subclusters using greedy randomized algorithm
Sandhya Shahdeo · OhioLink ETD Center (Ohio Library and Information Network) · 2011
Gene regulation, primarily achieved at the transcriptional level, is central to the normal development and functioning of all organisms.It therefore represents an obvious target for therapeutic drugs which could act either by stimulating or inhibiting specific gene transcription to elicit desired effects.Likewise, microRNAs, the recently discovered small non-coding RNAs, negatively regulate target genes post-transcriptionally through degradation or suppression of protein translation.Since both these regulators, endogenous and exogenous, affect biological and chemical pathways targeting gene regulation, the question is whether they can be used alternatively or combinatorially.Thus, hypothesizing that microRNAs-the endogenous regulators (mostly suppressors) of genes, could be used as alternatives to drugs or as combinatorials (with drugs) to fine tune the drug-response or mitigate potential side-effects, we used existing techniques like clustering, self organizing maps and computation of nearness in n dimensional space, to analyze the correlation between drugs and microRNAs.An algorithm to incorporate the heterogeneity of data by introducing a per data-species ratio, using a randomized greedy approach, was conceived.This algorithm finds the largest square matrices complying with the drug/microRNA ratio and density threshold by randomly selecting seed matrices and then systematically growing the best ones available and converges over multiple runs.iii vi 9.4 Limitations .....