Causal Reasoning and Machine Learning Models for Cellular Regulatory Mechanisms

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