In preparation

1. S-H. Jun, M. K. Halushka, and M. N. McCall. NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis. Under review at \emph{Biostatistics}.
   • Paper
2. O. Kviman, S-H. Jun, and J. Lagergren. LN’s t-Test: A Principled Approach to t-Testing in Single-Cell RNA Sequencing.
   • Paper.

Refereed publications

1. Y. Zheng, D. P. Caron, J.Y. Kim, S-H. Jun, Y. Tian, F. Mair, K. D. Stuart, P. A. Sims, R. Gottardo. ADTnorm: Robust integration of single-cell protein measurement across CITE-seq datasets. Nature Communications. Accepted.

2. A. M. Baran, A. H. Patil, E. Aparicio-Puerta, S-H. Jun, M. K. Halushka, M. N. McCall. miRglmm: a generalized linear mixed model of isomiR-level counts improves estimation of miRNA-level differential expression and uncovers variable differential expression between isomiRs. Genome Biol. 26, 102 (2025).

3. H. Koptagel, S-H. Jun, and J. Lagergren. SCuPhr: A Probabilistic Framework for Cell Lineage Tree Reconstruction. PLoS Computational Biology 20, e1012094 (2024).
   • Paper
4. S-H. Jun, H. Nasif, C. Jennings-Shaffer, D.H. Rich, A. Kooperberg, M. Fourment, C. Zhang, M.A. Suchard, and F.A. Matsen. A topology-marginal composite likelihood via a generalized phylogenetic pruning algorithm. Algorithms Molecular Biology 18, 10 (2023).
   • Paper
5. D. A. Oyong, F. J. Duffy, M. L. Neal, Y. Du, J. Carnes, K. V. Schwedhelm, N. Hertoghs, S-H. Jun, H. Miller, J. D. Aitchison, S. C. De Rosa, E. W. Newell, M. J. McElrath, S. M. McDermott, and K. D. Stuart. Distinct immune responses associated with vaccination status and protection outcomes after malaria challenge. PLOS pathogens. In press.
6. S-H. Jun, H. Toosi, J. Mold, C. Engblom, X. Chen, C. O’Flanagan, M. Hagemann-Jensen, R. Sandberg, S. Aparicio, J. Hartman, A. Roth, J. Lagergren, Reconstructing clonal tree for phylo-phenotypic characterization of cancer using single-cell transcriptomics. Nature Communications. 14, 982 (2023).
   • Paper
7. X. Chen, E.G. Sifakis, S. Robertson, S.Y. Neo, S-H. Jun, J. Lövrot, V. Jovic, J. Bergh, T. Foukakis, J. Lagergren, A. Lundqvist, R. Ma, and J. Hartman. Breast cancer patient-derived whole-tumor cell culture model for efficient drug profiling and treatment response prediction. Proceedings of the National Academy of Sciences of the United States of America. 120, e2209856120 (2023).
   • Paper
8. M. Mohaghegh Neyshabouri, S-H. Jun, and J. Lagergren. Inferring tumor progression in large datasets. PLOS Computational Biology. 16(10), p.e1008183 (2020).
   • Paper
9. S-H. Jun, S. Wong, J. Zidek, and A. Bouchard-Côté. Sequential decision model for inference and prediction on non-uniform hypergraphs with application to knot matching from computational forestry. The Annals of Applied Statistics. 13(3), pp. 1678-1707 (2019).
   • Paper
10. E. Haber, L. Ruthotto, E. Holtham, S-H. Jun. Learning across scales - A multiscale method for convolution neural networks. Association for the Advancement of Artificial Intelligence (AAAI). (2018).
    • Paper.
11. S-H. Jun, A. Bouchard-Côté, S. Wong, and J. Zidek. Sequential graph matching with sequential Monte Carlo. International Conference on Artificial Intelligence and Statistics (AISTATS). pp. 1075–1084 (2017).
    • Paper
12. S-H. Jun and A. Bouchard-Côté. Memory (and time) efficient sequential Monte Carlo. International Conference on Machine Learning (ICML). pp. 514-–522 (2014).
    • Paper, Poster
13. S-H. Jun, L. Wang, and A. Bouchard-Côté. Entangled Monte Carlo. Advances in Neural Information Processing Systems 25 (NIPS). pp. 2735–2743 (2012).
    • Paper, Poster