Hess M - Search Results

1

T-cell exhaustion and residency dynamics inform clinical outcomes in hepatocellular carcinoma.

Barsch M, Salié H, Schlaak AE, Zhang Z, Hess M, Mayer LS, Tauber C, Otto-Mora P, Ohtani T, Nilsson T, Wischer L, Winkler F, Manne S, Rech A, Schmitt-Graeff A, Bronsert P, Hofmann M, Neumann-Haefelin C, Boettler T, Fichtner-Feigl S, van Boemmel F, Berg T, Rimassa L, Di Tommaso L, Saeed A, D'Alessio A, Pinato DJ, Bettinger D, Binder H, John Wherry E, Schultheiss M, Thimme R, Bengsch B. Barsch M, et al. Among authors: hess m. J Hepatol. 2022 Aug;77(2):397-409. doi: 10.1016/j.jhep.2022.02.032. Epub 2022 Apr 1. J Hepatol. 2022. PMID: 35367533

2

Hepatic B cell leukemia-3 promotes hepatic steatosis and inflammation through insulin-sensitive metabolic transcription factors.

Gehrke N, Wörns MA, Huber Y, Hess M, Straub BK, Hövelmeyer N, Waisman A, Kim YO, Schuppan D, Galle PR, Schattenberg JM. Gehrke N, et al. Among authors: hess m. J Hepatol. 2016 Dec;65(6):1188-1197. doi: 10.1016/j.jhep.2016.06.026. Epub 2016 Jul 9. J Hepatol. 2016. PMID: 27405060

3

Partitioned learning of deep Boltzmann machines for SNP data.

Hess M, Lenz S, Blätte TJ, Bullinger L, Binder H. Hess M, et al. Bioinformatics. 2017 Oct 15;33(20):3173-3180. doi: 10.1093/bioinformatics/btx408. Bioinformatics. 2017. PMID: 28655145

4

The immune contexture of hepatocellular carcinoma predicts clinical outcome.

Foerster F, Hess M, Gerhold-Ay A, Marquardt JU, Becker D, Galle PR, Schuppan D, Binder H, Bockamp E. Foerster F, et al. Among authors: hess m. Sci Rep. 2018 Mar 29;8(1):5351. doi: 10.1038/s41598-018-21937-2. Sci Rep. 2018. PMID: 29599491 Free PMC article.

5

Exploring generative deep learning for omics data using log-linear models.

Hess M, Hackenberg M, Binder H. Hess M, et al. Bioinformatics. 2020 Dec 22;36(20):5045-5053. doi: 10.1093/bioinformatics/btaa623. Bioinformatics. 2020. PMID: 32647888 Free PMC article.

6

Synthetic observations from deep generative models and binary omics data with limited sample size.

Nußberger J, Boesel F, Lenz S, Binder H, Hess M. Nußberger J, et al. Among authors: hess m. Brief Bioinform. 2021 Jul 20;22(4):bbaa226. doi: 10.1093/bib/bbaa226. Brief Bioinform. 2021. PMID: 33003196

7

Deep generative models in DataSHIELD.

Lenz S, Hess M, Binder H. Lenz S, et al. Among authors: hess m. BMC Med Res Methodol. 2021 Apr 3;21(1):64. doi: 10.1186/s12874-021-01237-6. BMC Med Res Methodol. 2021. PMID: 33812380 Free PMC article.

8

Synthetic single cell RNA sequencing data from small pilot studies using deep generative models.

Treppner M, Salas-Bastos A, Hess M, Lenz S, Vogel T, Binder H. Treppner M, et al. Among authors: hess m. Sci Rep. 2021 Apr 30;11(1):9403. doi: 10.1038/s41598-021-88875-4. Sci Rep. 2021. PMID: 33931726 Free PMC article.

9

Stratified neural networks in a time-to-event setting.

Kuruc F, Binder H, Hess M. Kuruc F, et al. Among authors: hess m. Brief Bioinform. 2022 Jan 17;23(1):bbab392. doi: 10.1093/bib/bbab392. Brief Bioinform. 2022. PMID: 34585236

10

Interpretable generative deep learning: an illustration with single cell gene expression data.

Treppner M, Binder H, Hess M. Treppner M, et al. Among authors: hess m. Hum Genet. 2022 Sep;141(9):1481-1498. doi: 10.1007/s00439-021-02417-6. Epub 2022 Jan 6. Hum Genet. 2022. PMID: 34988661 Free PMC article. Review.

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