Date of Award
5-31-2018
Document Type
Campus Access Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Computer Science
First Advisor
Ping Chen
Second Advisor
Kourosh Zarringhalam
Third Advisor
Dan Simovici
Abstract
In this dissertation, we tackle problems in gene regulation and distance metric learning. In the first part of this thesis, we present three novel approaches for modeling transcriptional and post-transcriptional gene regulatory mechanisms. First, we propose a causal reasoning model for inferring upstream regulators of gene expression, including transcriptional regulators. Second, we propose a model for predicting small RNAs (sRNAs) in bacterial species that act as post-transcriptional regulators of the global regulator CsrA. Third, we propose a generalization of genome-wide association study (GWAS) over regulatory networks to identify functional pathways that are associated with a complex trait. Finally, in the second part of this thesis, we present a reformulation of the distance metric learning problem. All of our methods achieve good performance, are computationally efficient and are implemented in open-source R packages which can be installed from public repositories.
Recommended Citation
Fakhry, Carl Tony, "Causal Reasoning and Machine Learning Models for Cellular Regulatory Mechanisms" (2018). Graduate Doctoral Dissertations. 382.
https://scholarworks.umb.edu/doctoral_dissertations/382
Comments
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