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1.
Höllbacher, B. ; Strickland, B.* ; Greulich, F. ; Uhlenhaut, N.H. & Heinig, M.: Machine learning reveals STAT motifs as predictors for GR-mediated gene repression. Comp. Struc. Biotech. J. 21, 1697-1710 (2023)
2.
Ansari, S.A. et al.: Integrative analysis of macrophage ribo-Seq and RNA-Seq data define glucocorticoid receptor regulated inflammatory response genes into distinct regulatory classes. Comp. Struc. Biotech. J. 20, 5622-5638 (2022)
3.
Kupczyk, E. et al.: Unleashing high content screening in hit detection - Benchmarking AI workflows including novelty detection. Comp. Struc. Biotech. J. 20, 5453-5465 (2022)
4.
Luedemann, M.* et al.: Montelukast is a dual-purpose inhibitor of SARS-CoV-2 infection and virus-induced IL-6 expression identified by structure-based drug repurposing. Comp. Struc. Biotech. J. 20, 799-811 (2022)
5.
Margreiter, M.A.* et al.: Small-molecule modulators of TRMT2A decrease PolyQ aggregation and PolyQ-induced cell death. Comp. Struc. Biotech. J. 20, 443-458 (2022)
6.
Fino, R. et al.: Introducing the CSP analyzer: A novel machine learning-based application for automated analysis of two-dimensional NMR spectra in NMR fragment-based screening. Comp. Struc. Biotech. J. 18, 603-611 (2020)
7.
Höllbacher, B. ; Balazs, K. ; Heinig, M. & Uhlenhaut, N.H.: Seq-ing answers: Current data integration approaches to uncover mechanisms of transcriptional regulation. Comp. Struc. Biotech. J. 18, 1330-1341 (2020)
8.
Bartel, J. ; Krumsiek, J. & Theis, F.J.: Statistical methods for the analysis of high-throughput metabolomics data. Comp. Struc. Biotech. J. 4:e201301009 (2013)
9.
Brandmaier, S. & Tetko, I.V.: Robustness in experimental design: A study on the reliability of selection approaches. Comp. Struc. Biotech. J. 7:e201305002 (2013)
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