Contributor
Xinlei Wang, Tao Wang, Daniel F. Heitjan, Chul Moon, Raanju R. Sundararajan, Yi Han
Subject Area
Biostatistics, Immunology
Abstract
Due to the accumulation of a large volume of data of different natures such as sequencing data, proteomics data, and clinical data, statistical methods and deep learning algorithms have become increasingly important in the field of immunology. By leveraging the diverse datasets as well as interdisciplinary knowledge from areas like biology and public health, these quantitative methods have revolutionized this field by providing powerful tools for data analysis, modeling, and prediction. This has led to a deeper understanding of the immune system, accelerated the development of novel therapies, and paved the way for personalized and precision medicine approaches in immunology.
In this dissertation, we attempt to utilize Bayesian modeling techniques in conjunction with deep generative models to address emerging issues in immunology. Specifically, three models based on variational Bayes methods are devised for CyTOF data simulation, TCR-pMHC binding affinity prediction, and exploratory CyTOF data analysis. In chapter 2, Cytomulate, the first comprehensive simulation tool tailored for CyTOF data is proposed. pMTnet omni, which is detailed in chapter 3 carries the capability of differentiating binding and non-binding TCR-pMHC pairs. Finally, introduced in chapter 4, CytoOne provides a unified probabilistic framework for most CyTOF data analysis tasks.
Degree Date
Summer 8-6-2024
Document Type
Dissertation
Degree Name
Ph.D.
Department
Department of Statistics and Data Science
Advisor
Xinlei Wang
Second Advisor
Tao Wang
Third Advisor
Daniel F. Heitjan
Fourth Advisor
Chul Moon
Fifth Advisor
Raanju R. Sundararajan
Number of Pages
133
Format
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License
Recommended Citation
Yang, Yuqiu, "Bayesian and Deep Generative Modeling in Immunology" (2024). Statistical Science Theses and Dissertations. 47.
https://scholar.smu.edu/hum_sci_statisticalscience_etds/47
