Seifert M - Search Results

1

regNet: an R package for network-based propagation of gene expression alterations.

Seifert M, Beyer A. Seifert M, et al. Bioinformatics. 2018 Jan 15;34(2):308-311. doi: 10.1093/bioinformatics/btx544. Bioinformatics. 2018. PMID: 28968690

2

Meiotic cohesin SMC1β provides prophase I centromeric cohesion and is required for multiple synapsis-associated functions.

Biswas U, Wetzker C, Lange J, Christodoulou EG, Seifert M, Beyer A, Jessberger R. Biswas U, et al. Among authors: seifert m. PLoS Genet. 2013;9(12):e1003985. doi: 10.1371/journal.pgen.1003985. Epub 2013 Dec 26. PLoS Genet. 2013. PMID: 24385917 Free PMC article.

3

Importance of rare gene copy number alterations for personalized tumor characterization and survival analysis.

Seifert M, Friedrich B, Beyer A. Seifert M, et al. Genome Biol. 2016 Oct 3;17(1):204. doi: 10.1186/s13059-016-1058-1. Genome Biol. 2016. PMID: 27716417 Free PMC article.

4

CellTrans: An R Package to Quantify Stochastic Cell State Transitions.

Buder T, Deutsch A, Seifert M, Voss-Böhme A. Buder T, et al. Among authors: seifert m. Bioinform Biol Insights. 2017 Jun 16;11:1177932217712241. doi: 10.1177/1177932217712241. eCollection 2017. Bioinform Biol Insights. 2017. PMID: 28659714 Free PMC article.

5

Utilizing gene pair orientations for HMM-based analysis of promoter array ChIP-chip data.

Seifert M, Keilwagen J, Strickert M, Grosse I. Seifert M, et al. Bioinformatics. 2009 Aug 15;25(16):2118-25. doi: 10.1093/bioinformatics/btp276. Epub 2009 Apr 28. Bioinformatics. 2009. PMID: 19401402 Free PMC article.

6

Network-based analysis of oligodendrogliomas predicts novel cancer gene candidates within the region of the 1p/19q co-deletion.

Gladitz J, Klink B, Seifert M. Gladitz J, et al. Among authors: seifert m. Acta Neuropathol Commun. 2018 Jun 11;6(1):49. doi: 10.1186/s40478-018-0544-y. Acta Neuropathol Commun. 2018. PMID: 29890994 Free PMC article.

7

Network-based analysis of prostate cancer cell lines reveals novel marker gene candidates associated with radioresistance and patient relapse.

Seifert M, Peitzsch C, Gorodetska I, Börner C, Klink B, Dubrovska A. Seifert M, et al. PLoS Comput Biol. 2019 Nov 4;15(11):e1007460. doi: 10.1371/journal.pcbi.1007460. eCollection 2019 Nov. PLoS Comput Biol. 2019. PMID: 31682594 Free PMC article.

8

Comparative Gene Expression Analysis Reveals Similarities and Differences of Chronic Myeloid Leukemia Phases.

Schwarz A, Roeder I, Seifert M. Schwarz A, et al. Among authors: seifert m. Cancers (Basel). 2022 Jan 5;14(1):256. doi: 10.3390/cancers14010256. Cancers (Basel). 2022. PMID: 35008420 Free PMC article.

9

Computational gene expression analysis reveals distinct molecular subgroups of T-cell prolymphocytic leukemia.

Mikhaylenko N, Wahnschaffe L, Herling M, Roeder I, Seifert M. Mikhaylenko N, et al. Among authors: seifert m. PLoS One. 2022 Sep 21;17(9):e0274463. doi: 10.1371/journal.pone.0274463. eCollection 2022. PLoS One. 2022. PMID: 36129940 Free PMC article.

10

Exploiting prior knowledge and gene distances in the analysis of tumor expression profiles with extended Hidden Markov Models.

Seifert M, Strickert M, Schliep A, Grosse I. Seifert M, et al. Bioinformatics. 2011 Jun 15;27(12):1645-52. doi: 10.1093/bioinformatics/btr199. Epub 2011 Apr 21. Bioinformatics. 2011. PMID: 21511716

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