My lab’s research program at UChicago concerns the emerging field of cardioinformatics, working at the nexus of bioinformatics and precision cardiology. My principal research focus is on creating large-scale computational infrastructure for housing the world’s cardiovascular/renal/metabolic disease data at a massive scale, and mining new insights from it via secondary analysis of existing clinical and genetic datasets.
Previously, I was an American Heart Association (AHA) Postdoctoral Fellow working in the field of computational biology and cardiovascular medicine in the Gozani and Assimes Labs at Stanford University. My primary faculty advisor was Or Gozani MD, PhD (Department of Biology, Stanford University), and my secondary faculty mentor was Themistocles Assimes MD, PhD, FRCPC, FAHA (Department of Medicine, Cardiovascular Division, Stanford University School of Medicine). While at Stanford, I was involved in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Whole Genome Sequencing Program (Atherosclerosis Working Group), which represented the largest subclinical atherosclerosis and coronary artery disease study at the time (101,295 human genomes), and I served as a Research Associate in the VA Palo Alto Healthcare System working on cardiovascular disease genomics profiles in Korean and Vietnam war veterans enrolled in the Million Veteran Program. I also started and ran the Stanford R Group (http://www.stanfordr.com/), attracting top speakers from the R programming language community and growing to 350+ members.
Research highlights in my portfolio include:
Creating the world’s largest cardiovascular disease genetics database (https://www.ahajournals.org/doi/10.1161/CIRCGEN.118.002426 and https://doi.org/10.1101/2020.10.19.346445)
Computationally pinpointing and experimentally validating the epigenetic basis and biological mechanism underlying the cardioprotective genetic effects of ischemic preconditioning (https://doi.org/10.1161/JAHA.116.004076)
Writing a solo-author AI/machine learning paper on exploring the evolutionary consequences of codon usage bias in over 13,000 biological organisms (https://doi.org/10.1101/2020.10.26.356295)
Writing the world’s fastest biological heatmap software (https://doi.org/10.1371/journal.pone.0176334)
Designing the first cross-platform desktop GUI application for biological heatmaps (https://doi.org/10.1186/s13029-014-0030-2)
Showing that neural networks are essentially just polynomial regression in disguise (https://arxiv.org/abs/1806.06850)
Finding that drinking too much alcohol leads to the loss of an epigenetic enzyme (PRDM2) in the brain, which leads to loss of impulse control, which leads to alcoholism (https://www.nature.com/articles/mp2016131). Also, a follow-up on KDM6B published a few years later (https://onlinelibrary.wiley.com/doi/full/10.1111/adb.12816).
Supporting the Lisp programming community (https://doi.org/10.1093/bib/bbw130)
Writing more heatmap software, can you tell I like heatmaps? (https://doi.org/10.1186/s12859-016-1260-x)
Showing how EGFR inhibition represents a novel drug target to treat vascular calcification in patients with chronic kidney disease, a particularly hard therapeutic area that still to this day has no good drugs that can effectively slow down, prevent, or reverse kidney damage. Our lab’s cardioinformatics analyses of the MESA and Framingham cohorts showed that individuals with SNPs associated with increased serum concentrations of EGFR had elevated coronary artery calcium scores. We then tested and confirmed this experimentally in both in vitro cultures of human coronary artery smooth muscle cells AND in a murine model of chronic kidney disease, essentially tying together in silico with in vitro and in vivo work. (https://doi.org/10.1101/2021.11.08.467799)
Computationally exploring the role that the cytoskeleton (and the underlying extracellular matrix that supports its cellular architecture) can have to enable an entirely new class of cardiovascular therapeutics (https://doi.org/10.1080/17460441.2022.2047645)
Using single-cell RNA-seq approaches to discover new previously unclassified cell types in the heart (https://www.atherosclerosis-journal.com/article/S0021-9150(21)01464-7/fulltext)
Creating new biomedical natural language processing frameworks (https://www.nature.com/articles/s41540-021-00200-x)
Exploring the role of the microbiome in ear infections (https://www.nature.com/articles/s41522-021-00200-z)
Using WebAssembly to enable low latency interoperable augmented and virtual reality software (https://arxiv.org/abs/2110.07128)
TBA – lots of unpublished preprints (website likely not up-to-date)