Modern biotechnologies collect an ever-increasing amount of data about model organisms and humans. To facilitate data-driven discoveries in biology and medicine, I develop and apply statistical methods for large-scale experimental and observational studies. Particularly, I have extensive experience in analyzing genomics, proteomics, and clinical data to identify underlying signatures of diseases, molecular pathways, and environmental factors. My methodological research focuses on unsupervised statistical and machine learning, that can identify and utilize systematic patterns of variation.
B Mirza, W Wang, J Wang, H Choi, NC Chung, P Ping (2019). Machine learning and integrative analysis of biomedical big data. Genes
J Wang, H Choi, NC Chung, Q Cao, DCM Ng, B Mirza, SB Scruggs, D Wang, AO Garlid, P Ping (2018). Integrated dissection of the cysteine oxidative post-translational modification proteome during cardiac hypertrophy. Journal of Proteome Research; ProteomeXchange Data
HJ Painter, NC Chung, A Sebastian, I Albert, JD Storey, M Llinás (2018). Genome-wide real-time in vivo transcriptional dynamics during Plasmodium falciparum blood-stage development. Nature Communications; PlasmoDB; Figshare Data
NC Chung, C Bolton-Moore, R Chilengi, MP Kasaro, JSA Stringer, BH Chi (2017). Patient engagement in HIV care and treatment in Zambia, 2004–2014. Tropical Medicine & International Health
NC Chung, J Szyda, M Frąszczak, 1000 Bull Genomes Project (2017). Population structure analysis of bull genomes of european and western ancestry Scientific Reports; Consortium Site
NC Chung, JD Storey (2015). Statistical significance of variables driving systematic variation in high-dimensional data. Bioinformatics; R Software
J Kim, N Ghasemzadeh, DJ Eapen, NC Chung, JD Storey, AA Quyyumi, G Gibson (2014). Gene expression profiles associated with acute myocardial infarction and risk of cardiovascular death. Genome Medicine; GEO Data