Now showing items 1-6 of 6
Cell States and Cell Fate: Statistical and Computational Models in (Epi)Genomics
This dissertation develops and applies several statistical and computational methods to the analysis of Next Generation Sequencing (NGS) data in order to gain a better understanding of our biology. In the rest of the chapter ...
Statistical Methods for Large-Scale Integrative Genomics
In the past 20 years, we have witnessed a significant advance of high-throughput genetic and genomic technologies. With the massively generated genomics data, there is a pressing need for statistical methods that can utilize ...
Bayesian Statistical Framework for High-Dimensional Count Data and its Application in Microbiome Studies
High-dimensional count data arising from multinomial sampling is ubiquitous in microbiome studies. This dissertation aims to develop flexible Bayesian framework to model high-dimensional count data, which provides reliable ...
Methods for Estimating Hidden Structure and Network Transitions in Genomics
The explosion of data arising from advances in high throughput sequencing has allowed scientists to study genomics in far greater detail. However, this high resolution picture of cells often makes it difficult to see the ...
Quantifying Sources of Variation in High-throughput Biology
One of the central challenges in systems biology research is disentangling relevant and irrelevant sources of variation. While the relevant quantities are always context dependent, an important distinction can be drawn ...
Nucleotide-Level Modeling of Genetic Regulation Using Dilated Convolutional Neural Networks
The expression of genes is the product of a complex regulatory process, whose complete nature remains elusive. In order to better understand gene regulation, this work seeks to improve on efforts to model the locations of ...