Ka Yee Yeung, Ph.D.
About
Degrees
Introduction
My research focuses on the development of data science tools, their application to computational biology and the development of interoperable tools to enhance the reproducibility of research. My research focuses on the development of optimized methods and cloud-enabled software tools to facilitate the reproducible analyses of big biomedical data. I also develop machine learning methods that blend both computer science and statistics for applications in bioinformatics.
Current Research
I am the MPI of the NIH-funded MorPhiC (Molecular Phenotypes of Null Alleles in Cells, https://morphic.bio/) program. The goal of this program is to better understand the function of every human gene. My lab is primarily responsible for processing data generated in the MorPhiC program. My lab aims to deliver the latest technological advances in containers and cloud computing to a typical biomedical researcher with limited resources who works with big data. Specifically, we develop user-friendly and performance optimized software platforms to enable biomedical researchers to efficiently analyze and integrate diverse biomedical datasets. We have a training portal at https://biodepot.github.io/training/.
Honors and Awards
Recipient of the 2019 Distinguished Research Award at University of Washington Tacoma.
Rapid detection of myeloid neoplasm fusions using Single Molecule Long-Read Sequencing. Olga Sala-Torra, Shishir Reddy, Ling-Hong Hung, Lan Beppu, David Wu, Jerald Radich, Ka Yee Yeung, Cecilia CS Yeung. PLOS Global Public Health 3(9): e0002267.
Cloud-enabled Biodepot workflow builder integrates image processing using Fiji with reproducible data analysis using Jupyter notebooks. Ling-Hong Hung, Evan Straw, Shishir Reddy, Robert Schmitz, Zachary Colburn, and Ka Yee Yeung. Scientific Reports 12: 14920 (2022).
A graphical, interactive and GPU-enabled workflow to process long-read sequencing data. Shishir Reddy, Ling-Hong Hung, Olga Sala-Torra, Jerald Radich, Cecilia CS Yeung, Ka Yee Yeung. BMC Genomics 22, Article number: 626 (2021).
Building containerized workflows using the BioDepot-workflow-Builder (BwB). Ling-Hong Hung, Jiaming Hu, Trevor Meiss, Alyssa Ingersoll, Wes Lloyd, Daniel Kristiyanto, Yuguang Xiong, Eric Sobie, Ka Yee Yeung. Cell Systems 2019, volume 9, issue 5, pages 508-514.E3.
Holistic optimization of RNA-seq workflow for multi-threaded environments. Ling-Hong Hung, Wes Lloyd, Radhika Agumbe Sridhar, Saranya Devi Athmalingam Ravishankar, Yuguang Xiong, Eric Sobie, Ka Yee Yeung. Bioinformatics 2019, volume 35, issue 20, pages 4173-4175.
Reproducible Bioconductor Workflows Using Browser-Based Interactive Notebooks And Containers. Reem Almugbel, Ling-Hong Hung, Jiaming Hu, Abeer M. Almutairy, Nicole E. Ortogero, Yashaswi Tamta, Ka Yee Yeung. Journal of the American Medical Informatics Association (JAMIA) 2018, 25(1): 4-12 (Editor's Choice).
Model-based clustering with data correction for removing artifacts in gene expression data. William Chad Young, Ka Yee Yeung, Adrian E. Raftery. To appear in The Annals of Applied Statistics 2017. arXiv:1602.06316
GUIdock-VNC: Using a graphical desktop sharing system to provide a browser-based interface for containerized software. Varun Mittal, Ling-Hong Hung, Jayant Keswani, Daniel Kristiyanto, Sung Bong Lee and Ka Yee Yeung. Gigascience 2017, 6(4): 1-6.
fastBMA: Scalable Network Inference and Transitive Reduction. Ling-Hong Hung, Kaiyuan Shi, Migao Wu, William Chad Young, Adrian Raftery, Ka Yee Yeung. Gigascience 2017, gix078.
GUIdock: Using Docker containers with a common graphics user interface to address the reproducibility of research. Ling-Hong Hung, Daniel Kristiyanto, Sung Bong Lee, Ka Yee Yeung. PLOS One 2016, 11(4):e0152686.
A Posterior Probability Approach for Gene Regulatory Network Inference in Genetic Perturbation Data. William Chad Young, Adrian E. Raftery, Ka Yee Yeung. Mathematical Biosciences and Engineering (MBE) 2016, 13(6): 1241-1251.