Min Jin Ha

I received PhD in Biostatistics at University of North Carolina at Chapel Hill in 2013. I worked at the department of biostatistics, University of Texas MD Anderson Cancer Center 2016-2021. I am currently an assistant professor of biostatistics at Graduate School of Public Health, Yonsei University, Seoul, Korea. My main methodological research topic is graphical models in high-dimensional and multi-modal data, especially in cancer genomics. I am currently interested in sample-specific and robust inference of biological networks and the downstream modeling of networks with outcome data in the causal mediation analysis framework. As a biostatistician, I also enjoy collaborating with researchers in biomedicine.

email: mjha@yuhs.ac


See my google scholar profile for a full list of publications

(Bio)Statistical Methodologies (*corresponding author)

  1. Ha, M. J. & Sun, W. (2014). Partial correlation matrix estimation using ridge penalty followed by thresholding and re-estimation. Biometrics, 70(3), 762-770. [paper][software]

  2. Ha, M. J.*, Baladandayuthapani, V., & Do, K. A. (2015). Prognostic gene signature identification using causal structure learning: applications in kidney cancer. Cancer Informatics, 14(Suppl 1), 23. [paper]

  3. Ha, M. J., Baladandayuthapani, V., & Do, K. A. (2015). DINGO: differential network analysis in genomics. Bioinformatics, 31(21), 3413-3420. [paper][software]

  4. Ha, M. J., Sun, W., & Xie, J. (2016). PenPC: A two-step approach to estimate the skeletons of high-dimensional directed acyclic graphs. Biometrics, 72(1), 146-155. [paper][software]

  5. Class C.A., Ha M. J.*, Baladandayuthapani V. & Do K. (2017) iDINGO - Integrative differential network analysis in genomics with shiny application. Bioinformatics, 1, 3. [paper][software]

  6. Ni Y., Stingo F.C., Ha M. J., Akbani R., & Baladandayuthapani V. (2019) Bayesian hierarchical varying-sparsity regression models with application to cancer proteogenomics. Journal of the American Statistical Association. 114(525), 48-60.

  7. Kim J., Do K., Ha M. J., & Peterson C.B. (2019) Bayesian inference of hub nodes across multiple networks. Biometrics, 75(1), 172-182.

  8. Ha, M. J., Banerjee, S., Akbani, R., Liang, H., Mills, G. B., Do, K. A.,& Baladandayuthapani, V. (2018). Personalized Integrated Network Modeling of the Cancer Proteome Atlas. Scientific Reports, 8(1), 14924. [paper][shinyapp]

  9. Liu Q, Ha M. J., Bhattacharyya R. Garmire L., Baladandayuthapani V. Network-Based Matching of Patients and Targeted Therapies for Precision Oncology. (2020). Pacific Symposium on Biocomputing 2020.

  10. Bhattacharyya R., Ha M. J.*, Liu Q., Akbni R., Liang H., Baladandayuthapani V. Personalized Network Modeling of the Pan-Cancer Patient and Cell Line Interactome. (2020). Journal of Clinical Oncology, Clinical Cancer Informatics 4:399-411. [paper][shinyapp][codes]

  11. Ha, M.J.*, Kim, J., Galloway-Pena, J., Do, K.A. and Peterson, C.B., (2020). Compositional zero-inflated network estimation for microbiome data. BMC bioinformatics, 21(21), pp.1-20. [paper][software]

  12. Ha, M.J.* and Sun, W., (2020). Estimation of high-dimensional directed acyclic graphs with surrogate intervention. Biostatistics, 21(4), pp.659-675. [paper][software]

  13. Ha, M.J.*, Stingo, F.C. and Baladandayuthapani, V., (2021). Bayesian Structure Learning in Multilayered Genomic Networks. Journal of the American Statistical Association, pp.1-14. [paper][software]

  14. Saha, A., Ha, M.J., Acharyya, S. and Baladandayuthapani, V. (2021+) A Bayesian Precision Medicine Framework for Calibrating Individualized Therapeutic Indices in Cancer. Annals of Applied Statistics, To appear. [bioRxiv]

  15. Long, J.P. Ha ,M.J. (2021+) Sample Selection Bias in Evaluation of Prediction Performance of Causal Models. Statistical Analysis and Data Mining, Statistical Analysis and Data Mining 15 (1), 5-14. [paper]

  16. Huang L., Wang J., Fang B., Meric-Bernstam F.M., Roth J.A., Ha, M.J.* (2022) CombPDX: a unified statistical framework for evaluating drug synergism in patient-derived xenografts. Scientific Reports 12 (1),1-10. [paper][software]

Biomedical and Public Health Research (*corresponding author)

  1. Ha, M. J., Singareeka Raghavendra, A., Kettner, N. M., Qiao, W., Damodaran, S., Layman, R. M., ... & Keyomarsi, K. (2022). Palbociclib plus endocrine therapy significantly enhances overall survival of HR+/HER2- metastatic breast cancer patients compared to endocrine therapy alone in the second-line setting: A large institutional study. International Journal of Cancer, 70(3), 762-770. [paper]


Teaching

Statistics, Introduction to Bioinformatics (GS01113), MD Anderson Cancer Center UTHhealth Graduate School of Biomedical Sciences

Lab Members

  • Licai Huang, PHD Student, University of Texas MD Anderson Cancer Center, 2020-current

  • Moumita Chakraborty, Post-doctoral Fellow, University of Texas MD Anderson Cancer Center, 2019-current