Bio

Institute Affiliations


  • Member, Maternal & Child Health Research Institute (MCHRI)

Professional Education


  • Doctor of Science, Julius Maximilians Univsitat (2018)
  • Diplom, Julius Maximilians Univsitat (2011)

Publications

All Publications


  • Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions NATURE MACHINE INTELLIGENCE Culos, A., Tsai, A. S., Stanley, N., Becker, M., Ghaemi, M. S., McIlwain, D. R., Fallahzadeh, R., Tanada, A., Nassar, H., Espinosa, C., Xenochristou, M., Ganio, E., Peterson, L., Han, X., Stelzer, I. A., Ando, K., Gaudilliere, D., Phongpreecha, T., Maric, I., Chang, A. L., Shaw, G. M., Stevenson, D. K., Bendall, S., Davis, K. L., Fantl, W., Nolan, G. P., Hastie, T., Tibshirani, R., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2020
  • OpenLUR: Off-the-shelf air pollution modeling with open features and machine learning ATMOSPHERIC ENVIRONMENT Lautenschlager, F., Becker, M., Kobs, K., Steininger, M., Davidson, P., Krause, A., Hotho, A. 2020; 233
  • Multi-Omic, Longitudinal Profile of Third-Trimester Pregnancies Identifies a Molecular Switch That Predicts the Onset of Labor. Stelzer, I., Ghaemi, M., Han, X., Ando, K., Peterson, L., Contrepois, K., Ganio, E., Tsai, A., Tsai, E., Rumer, K., Stanley, N., Fallazadeh, R., Becker, M., Culos, A., Gaudilliere, D., Wong, R., Winn, V., Shaw, G., Stevenson, D., Snyder, M., Angst, M., Aghaeepour, N., Gaudilliere, B. SPRINGER HEIDELBERG. 2020: 89A
  • VoPo leverages cellular heterogeneity for predictive modeling of single-cell data. Nature communications Stanley, N., Stelzer, I. A., Tsai, A. S., Fallahzadeh, R., Ganio, E., Becker, M., Phongpreecha, T., Nassar, H., Ghaemi, S., Maric, I., Culos, A., Chang, A. L., Xenochristou, M., Han, X., Espinosa, C., Rumer, K., Peterson, L., Verdonk, F., Gaudilliere, D., Tsai, E., Feyaerts, D., Einhaus, J., Ando, K., Wong, R. J., Obermoser, G., Shaw, G. M., Stevenson, D. K., Angst, M. S., Gaudilliere, B., Aghaeepour, N. 2020; 11 (1): 3738

    Abstract

    High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.

    View details for DOI 10.1038/s41467-020-17569-8

    View details for PubMedID 32719375

Footer Links:

Stanford Medicine Resources: