02.01.2019
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MCML Researchers in Highly-Ranked Journals
Five Papers in 2019 Highlight Scientific Impact
We are happy to announce that MCML researchers are represented in 2019 with five papers in highly-ranked journals. Congrats to our researchers!
L. M. Weber • W. Saelens • R. Cannoodt • C. Soneson • A. Hapfelmeier • P. P. Gardner • A.-L. Boulesteix • Y. Saeys • M. D. Robinson
Essential guidelines for computational method benchmarking.
Genome Biology 20.125. Jun. 2019. DOI
Essential guidelines for computational method benchmarking.
Genome Biology 20.125. Jun. 2019. DOI
P. Probst • A.-L. Boulesteix • B. Bischl
Tunability: Importance of Hyperparameters of Machine Learning Algorithms.
Journal of Machine Learning Research 20. Mar. 2019. PDF
Tunability: Importance of Hyperparameters of Machine Learning Algorithms.
Journal of Machine Learning Research 20. Mar. 2019. PDF
M. D. Luecken • F. J. Theis
Current best practices in single‐cell RNA‐seq analysis: a tutorial.
Molecular Systems Biology 15.e8746. Jun. 2019. DOI GitHub
Current best practices in single‐cell RNA‐seq analysis: a tutorial.
Molecular Systems Biology 15.e8746. Jun. 2019. DOI GitHub
M. Lotfollahi • F. A. Wolf • F. J. Theis
scGen predicts single-cell perturbation responses.
Nature Methods 16.8. Jul. 2019. DOI GitHub
scGen predicts single-cell perturbation responses.
Nature Methods 16.8. Jul. 2019. DOI GitHub
F. Erhard • M. A. P. Baptista • T. Krammer • T. Hennig • M. Lange • P. Arampatzi • C. S. Jürges • F. J. Theis • A.-E. Saliba • L. Dölken
scSLAM-seq reveals core features of transcription dynamics in single cells.
Nature 571. Jul. 2019. DOI
scSLAM-seq reveals core features of transcription dynamics in single cells.
Nature 571. Jul. 2019. DOI
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