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Berndt, N.; Kolbe, E.; Gajowski, R.; Eckstein, J.; Ott, F.; Meierhofer, D.; Holzhütter, H. G.; Matz‐Soja, M.: Functional consequences of metabolic zonation in murine livers: New insights for an old story. Hepatology 73 (2), HEP-19-1360, pp. 795 - 810 (2021)
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Reichenbach, M.; Mendez, P.-L.; da Silva Madaleno, C.; Ugorets, V.; Rikeit, P.; Boerno, S.; Jatzlau, J.; Knaus, P.: Differential Impact of Fluid Shear Stress and YAP/TAZ on BMP/TGF-β Induced Osteogenic Target Genes. Advanced Biology 5 (2), 2000051 (2021)
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Kraushar, M. L.; Krupp, F.; Harnett, D.; Turko, P.; Ambrozkiewicz, M. C.; Sprink, T.; Imami, K.; Günnigmann, M.; Zinnall, U.; Vieira-Vieira, C. H. et al.; Schaub, T.; Münster-Wandowski, A.; Bürger, J.; Borisova, E.; Yamamot, H.; Rasin, M.-R.; Ohlen, U.; Beule, D.; Mielke, T.; Tarabykin, V.; Landthaler, M.; Kramer, G.; Vida, I.; Selbach, M.; Spahn, C. M. T.: Protein Synthesis in the Developing Neocortex at Near-Atomic Resolution Reveals Ebp1-Mediated Neuronal Proteostasis at the 60S Tunnel Exit. Molecular Cell 81 (2), pp. 304 - 322 (2021)
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Beheshtian, M.; Akhtarkhavari, T.; Mehvari, S.; Mohseni, M.; Fattahi, Z.; Abedini, S. S.; Arzhangi , S.; Fadaee, M.; Jamali , P.; Najafipour, R. et al.; Kalscheuer, V. M.; Hu, H.; Ropers, H.-H.; Najmabadi, H.; Kahrizi, K.: Comprehensive genotype‐phenotype correlation in AP‐4 deficiency syndrome; Adding data from a large cohort of Iranian patients. Clinical Genetics: an international journal of genetics in medicine 99 (1), pp. 187 - 192 (2021)
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Farrall, A.; Lienhard, M.; Grimm, C.; Kuhl, H.; Sluka, S. H. M.; Caparros Rodriguez, M.; Forejt, J.; Timmermann, B.; Herwig, R.; Herrmann, B. G. et al.; Morkel, M.: PWD/Ph-encoded genetic variants modulate the cellular Wnt/β-Catenin response to suppress ApcMin-triggered intestinal tumor formation. Cancer research: an official organ of the American Association for Cancer Research 81 (1), pp. 38 - 49 (2021)
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Kretzmer, H.; Biran, A.; Purroy, N.; Lemvigh, C. K.; Clement, K.; Gruber, M.; Gu, H.; Rassenti, L.; Mohammad, A. W.; Lesnick, C. et al.; Slager, S. L.; Braggio, E.; Shanafelt, T. D.; Kay, N. E.; Fernandes, S. M.; Brown, J. R.; Wang, L.; Li, S.; Livak, K. J.; Neuberg, D. S.; Klages, S.; Timmermann, B.; Kipps, T. J.; Campo, E.; Gnirke, A.; Wu, C. J.; Meissner, A.: Pre-neoplastic alterations define CLL DNA methylome and persist through disease progression and therapy. Blood Cancer Discovery 2 (1), pp. 54 - 69 (2021)
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Said, N.; Hilal, T.; Sunday, N. D.; Khatri, A.; Bürger, J.; Mielke, T.; Belogurov, G. A.; Loll, B.; Sen, R.; Artsimovitch, I. et al.; Wahl, M. C.: Steps toward translocation-independent RNA polymerase inactivation by terminator ATPase ρ. Science 371 (6524), eabd1673 (2021)

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Jasnovidova, O.; Arnold, M.; Mayer, A.: Illuminating enhancer transcription at nucleotide resolution with native elongating transcript sequencing (NET-Seq). In: Enhancers and Promoters - Methods and Protocols (Methods in Molecular Biology ; 2351), Vol. 2351, pp. 41 - 65 (Ed. Borggrefe, T.). Humana, New York, NY (2021)

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Klarner, H.; Tonello, E.; Cifuentes Fontanals, L.; Janody, F.; Chaouiya, C.; Siebert, H.: Detection of markers for discrete phenotypes. In: CSBio2021, pp. 64 - 68. CSBio2021: The 12th International Conference on Computational Systems-Biology and Bioinformatics, Virtual Event, October 14, 2021 - October 15, 2021. Association for Computing Machinery, New York, NY (2021)

Thesis - PhD (9)

96.
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Schulte-Sasse, R.: Integration of multi-omics data with graph convolutional networks to identify cancer-associated genes. Dissertation, ix, 190 pp. (2021)
97.
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Bothe, A. M.: Investigating the Genomic Effects of Glucocorticoid Receptor Activation: An Analysis of Transcriptional Memory and Mechanisms That Direct Divergent Genomic Occupancy of Related Transcription Factors. Dissertation, xxi, 93 pp. (2021)
98.
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Barros de Andrade e Sousa, L.: Using interpretable machine learning to understand gene silencing dynamics during X-chromosome inactivation. Dissertation, vii, 159 pp. (2021)
99.
Thesis - PhD
Rodrigues de Melo Costa, V.: Genome-wide Determination Of Splicing Efficiency And Dynamics From RNA-Seq Data. Dissertation, ix, 100 pp. (2021)
100.
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Budach, S.: Explainable deep learning models for biological sequence classification. Dissertation, vii, 119 pp. (2021)
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