Yongjin Park

 
Prospective Graduate Students / Postdocs

This faculty member is currently not actively recruiting graduate students or Postdoctoral Fellows, but might consider co-supervision together with another faculty member.

Assistant Professor

Research Interests

single-cell genomics
Computational Biology
Causal inference
Bayesian machine learning

Relevant Thesis-Based Degree Programs

Affiliations to Research Centres, Institutes & Clusters

Research Options

I am available and interested in collaborations (e.g. clusters, grants).
I am interested in and conduct interdisciplinary research.
 
 

Research Methodology

bioinformatics tool development
Bayesian modelling
Causal inference
probabilistic programming

Graduate Student Supervision

Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

LiquidBayes : a bayesian network for monitoring cancer progression using liquid biopsies (2023)

The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.

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False discovery rate estimation for high-dimensional regression models (2022)

A genome-wide association study (GWAS) aims to determine genetic variants statistically associated with phenotypes. However, because of linkage disequilibrium (LD), a characteristic of large-scale genomic datasets referring to the strong local dependencies between single-nucleotide polymorphisms (SNPs), it is usually challenging to identify the actual causal variants among their associated proxies. In this work, we propose a Bayesian variable selection method called the sparse mixed Gaussian prior for generalized linear models (SMG-GLM). It is an efficient high-dimensional Bayesian variable selection approach designed for arbitrary relationships between variants and phenotypes. Besides, it calibrates the selection uncertainty, which many popular variable selection methods do not address, by estimating posterior inclusion probabilities. We additionally combine SMG-GLM with knockoffs, named SMG-knockoffs, to account for the collinearity problem caused by LD. The SMG-knockoffs method can make inferences on the variable selection result and control the false discovery rate at an expected level. Its competence in discovering causal variables while controlling a desired false discovery rate has been shown in simulation studies conducted on a GWAS dataset.

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