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Blasi, T. ; Buettner, F. ; Strasser, M. ; Marr, C. & Theis, F.J.: CgCorrect: A method to correct for confounding cell-cell variation due to cell growth in single-cell transcriptomics. Phys. Biol. 14:036001 (2017)
Eulenberg, P. et al.: Reconstructing cell cycle and disease progression using deep learning. Nat. Commun. 8:463 (2017)
Blasi, T. et al.: Label-free cell cycle analysis for high-throughput imaging flow cytometry. Nat. Commun. 7:10256 (2016)
Blasi, T. et al.: Combinatorial histone acetylation patterns are generated by motif-specific reactions. Cell Syst. 2, 49-58 (2016)
Blasi, T.: Data-driven statistical learning to model cellular heterogeneity. München, Technische Universität, Fakultät für Mathematik, Diss., 2016, 178 S.
Hennig, H.* et al.: An open-source solution for advanced imaging flow cytometry data analysis using machine learning. Methods 112, 201-210 (2016)