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Dissertation
Inference of gene regulation from expression datasets
Doctor of Philosophy (Ph.D.), Drexel University
01 Jun 2015
DOI:
https://doi.org/10.17918/etd-6412
Abstract
The development of high throughput techniques and the accumulation of large scale gene expression data provide researchers great opportunities to more efficiently solve important but complex biological problems, such as reconstruction of gene regulatory networks and identification of miRNA-target interactions. In the past decade, many algorithms have been developed to address these problems. However, prediction and simulation of gene expression data have not yet received as much attention. In this study, we present a model based on stepwise multiple linear regression (SMLR) that can be applied for prediction and simulation of gene expression, as well as reconstruction of gene regulatory networks by analysis of time-series gene expression data, and we present its application in analysis of paired miRNA-mRNA expression data.
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Details
- Title
- Inference of gene regulation from expression datasets
- Creators
- Yiqian Zhou - DU
- Contributors
- Ahmet Sacan (Advisor) - Drexel University (1970-)
- Awarding Institution
- Drexel University
- Degree Awarded
- Doctor of Philosophy (Ph.D.)
- Publisher
- Drexel University; Philadelphia, Pennsylvania
- Resource Type
- Dissertation
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems (1997-2026); Drexel University
- Other Identifier
- 6412; 991014632430104721