Ho-Joon Lee, PhD
Research Scientist in GeneticsCards
About
Research
Overview
Two primary projects are (1) AI/ML of ischemic stroke etiology classification using electronic health records and MRI images in collaboration with Dr. Richa Sharma (1 patent pending; https://www.nature.com/articles/s41746-024-01120-w) (2) AI/ML meta-modeling for ligand-protein binding affinity prediction with the Gerstein lab (1 patent pending; https://pubs.acs.org/doi/10.1021/acs.jcim.4c01116) and drug discovery applications for targeted protein degradation in collaboration with the Spiegel lab.
Previous projects include (1) single-cell systems immunology of West Nile virus infection with the Montgomery and Kleinstein labs (https://www.cell.com/iscience/fulltext/S2589-0042(23)02464-1) (2) single-cell multi-omics data analysis of zebrafish brains and embryos with the Giraldez lab (https://elifesciences.org/arti...).
In response to the COVID-19 pandemic, I worked on virus-host protein-protein interactions (PPIs) and computational drug discovery together with Dr. Prashant Emani (the Gerstein lab) as a COVID HASTE working group of the Yale School of Engineering and Applied Science (see Figure 1 below for an overview). The project on virus-host PPIs was partially supported by a seed grant from the Northeast Big Data Innovation Hub and a preprint has been published (https://nebigdatahub.org/a-lan...). As an additional effort, with help from two of my former colleagues, Dr. Vinayagam Arunachalam (Takeda Pharmaceuticals) and Dr. Yang-Yu Liu (Harvard Medical School, Brigham and Women's Hospital), I carried out controllability analysis of a directed human protein-protein interaction network for SARS-CoV-2 based on our previous paper (https://www.pnas.org/doi/10.10...). A preprint is available here, https://www.biorxiv.org/conten....
My previous research mostly concerned biological questions in systems biology and network medicine by developing algorithms and models through a combination of statistical/machine learning, information theory, and network theory applied to high-throughput multi-dimensional data. It covered genomics, transcriptomics, proteomics, and metabolomics from yeast to mouse to human for integrative analysis of regulatory networks on multiple molecular levels. I previously carried out proteomics and metabolomics along with a computational derivation of dynamic protein complexes for IL-3 activation and cell cycle in murine pro-B cells (https://www.cell.com/cell-repo...; see Figure 2 below), for which I developed integrative analytical tools using diverse approaches from machine learning and network theory. My ongoing interests in methodology include machine/deep learning and topological Kolmogorov-Sinai entropy-based network theory, which are applied to (1) multi-level dynamic regulatory networks in immune response, cell cycle, cancer metabolism, and cell fate decision and (2) single cell-based and mass spectrometry-based omics data analysis. Two specific projects were (1) Dynamic metabolic network modeling of a mammalian cell cycle using multi-omics time-course data in collaboration with the Chandrasekaran lab at the University of Michigan (see Figure 3 below; https://www.biorxiv.org/conten...; https://www.cell.com/iscience/...) and (2) Tri-omics analysis of macrophage polarization in pancreatic cancer in collaboration with the Lyssiotis lab at the University of Michigan Medical School (https://elifesciences.org/arti...).
Medical Research Interests
Public Health Interests
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- This is an overview of our general strategy to study SARS-CoV-2 infection by integrative computational approaches in the context of systems biology and network medicine.
- (A) Multi-omics abundance profiles of proteins, modules/complexes, intracellular metabolites, and extracellular metabolites over one cell cycle (from left to right columns) in response to IL-3 activation. Red for proteins/modules/intracellular metabolites up-regulation or extracellular metabolites release; Green for proteins/modules/intracellular metabolites down-regulation or extracellular metabolites uptake. (B) Functional module network identified from integrative analysis. Red nodes are proteins and white nodes are functional modules. Expression profile plots are shown for literature-validated functional modules. (C) Overall pathway map of IL-3 activation and cell cycle phenotypes. (D) IL-3 activation and cell cycle as a cancer model along with candidate protein and metabolite biomarkers. (E) Protein co-expression scale-free network. (F) Power-low degree distribution of the network E. (G) Protein entropy distribution by topological Kolmogorov-Sinai entropy calculated for the network E.
- Dynamic metabolic network modeling of a cytokine-induced mammalian cell cycle using time-course metabolomics and proteomics. Presented at the 68th ASMS Conference on Mass Spectrometry and Allied Topics 2020.