Outputs (3)

Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics (2019)
Journal Article
Warnat-Herresthal, S., Perrakis, K., Taschler, B., Becker, M., Baßler, K., Beyer, M., Günther, P., Schulte-Schrepping, J., Seep, L., Klee, K., Ulas, T., Haferlach, T., Mukherjee, S., & Schultze, J. L. (2020). Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics. iScience, 23(1), Article 100780. https://doi.org/10.1016/j.isci.2019.100780

Acute myeloid leukemia (AML) is a severe, mostly fatal hematopoietic malignancy. We were interested in whether transcriptomic-based machine learning could predict AML status without requiring expert input. Using 12,029 samples from 105 different stud... Read More about Scalable prediction of acute myeloid leukemia using high-dimensional machine learning and blood transcriptomics.

Variations of power-expected-posterior priors in normal regression models (2019)
Journal Article
Fouskakis, D., Ntzoufras, I., & Perrakis, K. (2020). Variations of power-expected-posterior priors in normal regression models. Computational Statistics & Data Analysis, 143, Article 106836. https://doi.org/10.1016/j.csda.2019.106836

The power-expected-posterior (PEP) prior is an objective prior for Gaussian linear models, which leads to consistent model selection inference, under the M-closed scenario, and tends to favour parsimonious models. Recently, two new forms of the PEP p... Read More about Variations of power-expected-posterior priors in normal regression models.

Scalable Bayesian regression in high dimensions with multiple data sources (2019)
Journal Article
Perrakis, K., Mukherjee, S., & Initiative, T. A. D. N. (2020). Scalable Bayesian regression in high dimensions with multiple data sources. Journal of Computational and Graphical Statistics, 29(1), 28-39. https://doi.org/10.1080/10618600.2019.1624294

Applications of high-dimensional regression often involve multiple sources or types of covariates. We propose methodology for this setting, emphasizing the “wide data” regime with large total dimensionality p and sample size n≪p. We focus on a flexib... Read More about Scalable Bayesian regression in high dimensions with multiple data sources.