DeTangle: A Framework for Interactive Prediction and Visualization of Gene Regulatory Networks
| dc.contributor.author | Altarawy, Doaa Abdelsalam Ahmed Mohamed | en |
| dc.contributor.committeechair | Heath, Lenwood S. | en |
| dc.contributor.committeemember | North, Christopher L. | en |
| dc.contributor.committeemember | Grene, Ruth | en |
| dc.contributor.committeemember | Shaffer, Clifford A. | en |
| dc.contributor.committeemember | Ismail, Mahamed | en |
| dc.contributor.department | Computer Science | en |
| dc.date.accessioned | 2018-10-25T06:00:55Z | en |
| dc.date.available | 2018-10-25T06:00:55Z | en |
| dc.date.issued | 2017-05-02 | en |
| dc.description.abstract | With the abundance of biological data, computational prediction of gene regulatory networks (GRNs) from gene expression data has become more feasible. Although incorporating other prior knowledge (PK), along with gene expression, greatly improves prediction accuracy, the accuracy remains low. PK in GRN inference can be categorized into noisy and curated. Several algorithms were proposed to incorporate noisy PK, but none address curated PK. Another challenge is that much of the PK is not stored in databases or not in a unified structured format to be accessible by inference algorithms. Moreover, no GRN inference method exists that supports post-prediction PK. This thesis addresses those limitations with three solutions: PEAK algorithm for integrating both curated and noisy PK, Online-PEAK for post-prediction interactive feedback, and DeTangle for visualization and navigation of GRNs. PEAK integrates both curated as well as noisy PK in GRN inference. We introduce a novel method for GRN inference, CurInf, to effectively integrate curated PK, and we use the previous method, Modified Elastic Net, for noisy PK, and we call it NoisInf. Using 100% curated PK, CurInf improves the AUPR accuracy score over NoisInf by 27.3% in synthetic data, 86.5% in E. coli data, and 31.1% in S. cerevisiae data. Moreover, we developed an online algorithm, online-PEAK, that enables the biologist to interact with the inference algorithm, PEAK, through a visual interface to add their domain experience about the structure of the GRN as a feedback to the system. We experimentally verified the ability of online-PEAK to achieve incremental accuracy when PK is added by the user, including true and false PK. Even when the noise in PK is 10 times more than true PK, online-PEAK performs better than inference without any PK. Finally, we present DeTangle, a Web server for interactive GRN prediction and visualization. DeTangle provides a seamless analysis of GRN starting from uploading gene expression, GRN inference, post-prediction feedback using online-PEAK, and visualization and navigation of the predicted GRN. More accurate prediction of GRN can facilitate studying complex molecular interactions, understanding diseases, and aiding drug design. | en |
| dc.description.abstractgeneral | Proteins are complex molecules that are responsible for most of the functionalities in living organisms. Proteins are produced from genes inside the cells. The production of proteins can be controlled by other proteins causing their production to increase or to decrease. This control is called gene regulation. Studying gene regulation between genes is important because it facilitates understanding diseases and aiding drug design. In this thesis, I developed computational methods, called PEAK and online-PEAK, to predict the interactions between genes using computer algorithms. My test results show that the method PEAK is more accurate than previous existing methods. I have also built a Web application, DeTangle, to help biologists use the algorithm and visualize the results. | en |
| dc.description.degree | Ph. D. | en |
| dc.format.medium | ETD | en |
| dc.identifier.other | vt_gsexam:11346 | en |
| dc.identifier.uri | http://hdl.handle.net/10919/85504 | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | In Copyright | en |
| dc.rights.uri | http://rightsstatements.org/vocab/InC/1.0/ | en |
| dc.subject | Gene regulation | en |
| dc.subject | prior knowledge | en |
| dc.subject | gene regulatory network inference | en |
| dc.subject | visualization | en |
| dc.subject | Machine learning | en |
| dc.title | DeTangle: A Framework for Interactive Prediction and Visualization of Gene Regulatory Networks | en |
| dc.type | Dissertation | en |
| thesis.degree.discipline | Computer Science and Applications | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | doctoral | en |
| thesis.degree.name | Ph. D. | en |
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