Neo Christopher Chung

Statistical and Machine Learning for Biology and Medicine

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 and machine learning 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.

PI: Neo Christopher Chung
Assistant Professor, Institute of Informatics, University of Warsaw
Visiting Scientist, Department of Physiology and Medicine, University of California Los Angeles

Ph.D./M.A. in Quantitative and Computational Biology, Princeton University
B.S.E. in Biomedical Engineering, Duke University

        

Selected Publications
NC Chung (2020) Statistical significance of cluster membership for unsupervised evaluation of cell identities. Bioinformatics; R Software
L Brocki, NC Chung (2019). Concept saliency maps to visualize relevant features in deep generative models. IEEE ICMLA; Jupyter Notebooks
HJ Painter, NC Chung, A Sebastian, I Albert, JD Storey, M LlinĂ¡s (2018). Genome-wide real-time in vivo transcriptional dynamics during Plasmodium falciparum blood-stage development. Nature Communications; PlasmoDB; Figshare Data
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
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