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ucin1298042586.pdf (2.49 MB)
ETD Abstract Container
Abstract Header
MiR-Drug Relationships: Mining and discovering bi-domain dense subclusters using greedy randomized algorithm
Author Info
Shahdeo, Sandhya
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1298042586
Abstract Details
Year and Degree
2011, MS, University of Cincinnati, Engineering and Applied Science: Computer Science.
Abstract
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.
Committee
Yizong Cheng, PhD (Committee Chair)
Anil Jegga, DVM, MRes (Committee Member)
Raj Bhatnagar, PhD (Committee Member)
Pages
65 p.
Subject Headings
Computer Science
Keywords
microRNA drug relation
;
greedy randomized algorithm
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Citations
Shahdeo, S. (2011).
MiR-Drug Relationships: Mining and discovering bi-domain dense subclusters using greedy randomized algorithm
[Master's thesis, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1298042586
APA Style (7th edition)
Shahdeo, Sandhya.
MiR-Drug Relationships: Mining and discovering bi-domain dense subclusters using greedy randomized algorithm.
2011. University of Cincinnati, Master's thesis.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1298042586.
MLA Style (8th edition)
Shahdeo, Sandhya. "MiR-Drug Relationships: Mining and discovering bi-domain dense subclusters using greedy randomized algorithm." Master's thesis, University of Cincinnati, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1298042586
Chicago Manual of Style (17th edition)
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Document number:
ucin1298042586
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696
Copyright Info
© 2010, some rights reserved.
MiR-Drug Relationships: Mining and discovering bi-domain dense subclusters using greedy randomized algorithm by Sandhya Shahdeo is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Cincinnati and OhioLINK.