Jurtz VI - Search Results

1

HostPhinder: A Phage Host Prediction Tool.

Villarroel J, Kleinheinz KA, Jurtz VI, Zschach H, Lund O, Nielsen M, Larsen MV. Villarroel J, et al. Among authors: jurtz vi. Viruses. 2016 May 4;8(5):116. doi: 10.3390/v8050116. Viruses. 2016. PMID: 27153081 Free PMC article.

2

A Bacterial Analysis Platform: An Integrated System for Analysing Bacterial Whole Genome Sequencing Data for Clinical Diagnostics and Surveillance.

Thomsen MC, Ahrenfeldt J, Cisneros JL, Jurtz V, Larsen MV, Hasman H, Aarestrup FM, Lund O. Thomsen MC, et al. PLoS One. 2016 Jun 21;11(6):e0157718. doi: 10.1371/journal.pone.0157718. eCollection 2016. PLoS One. 2016. PMID: 27327771 Free PMC article.

3

MetaPhinder-Identifying Bacteriophage Sequences in Metagenomic Data Sets.

Jurtz VI, Villarroel J, Lund O, Voldby Larsen M, Nielsen M. Jurtz VI, et al. PLoS One. 2016 Sep 29;11(9):e0163111. doi: 10.1371/journal.pone.0163111. eCollection 2016. PLoS One. 2016. PMID: 27684958 Free PMC article.

4

NetH2pan: A Computational Tool to Guide MHC Peptide Prediction on Murine Tumors.

DeVette CI, Andreatta M, Bardet W, Cate SJ, Jurtz VI, Jackson KW, Welm AL, Nielsen M, Hildebrand WH. DeVette CI, et al. Among authors: jurtz vi. Cancer Immunol Res. 2018 Jun;6(6):636-644. doi: 10.1158/2326-6066.CIR-17-0298. Epub 2018 Apr 3. Cancer Immunol Res. 2018. PMID: 29615400 Free PMC article.

5

Machine learning reveals a non-canonical mode of peptide binding to MHC class II molecules.

Andreatta M, Jurtz VI, Kaever T, Sette A, Peters B, Nielsen M. Andreatta M, et al. Among authors: jurtz vi. Immunology. 2017 Oct;152(2):255-264. doi: 10.1111/imm.12763. Epub 2017 Jun 19. Immunology. 2017. PMID: 28542831 Free PMC article.

6

An introduction to deep learning on biological sequence data: examples and solutions.

Jurtz VI, Johansen AR, Nielsen M, Almagro Armenteros JJ, Nielsen H, Sønderby CK, Winther O, Sønderby SK. Jurtz VI, et al. Bioinformatics. 2017 Nov 15;33(22):3685-3690. doi: 10.1093/bioinformatics/btx531. Bioinformatics. 2017. PMID: 28961695 Free PMC article.

7

NetSurfP-2.0: Improved prediction of protein structural features by integrated deep learning.

Klausen MS, Jespersen MC, Nielsen H, Jensen KK, Jurtz VI, Sønderby CK, Sommer MOA, Winther O, Nielsen M, Petersen B, Marcatili P. Klausen MS, et al. Among authors: jurtz vi. Proteins. 2019 Jun;87(6):520-527. doi: 10.1002/prot.25674. Epub 2019 Mar 9. Proteins. 2019. PMID: 30785653

8

NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data.

Jurtz V, Paul S, Andreatta M, Marcatili P, Peters B, Nielsen M. Jurtz V, et al. J Immunol. 2017 Nov 1;199(9):3360-3368. doi: 10.4049/jimmunol.1700893. Epub 2017 Oct 4. J Immunol. 2017. PMID: 28978689 Free PMC article.

9

A generic deep convolutional neural network framework for prediction of receptor-ligand interactions-NetPhosPan: application to kinase phosphorylation prediction.

Fenoy E, Izarzugaza JMG, Jurtz V, Brunak S, Nielsen M. Fenoy E, et al. Bioinformatics. 2019 Apr 1;35(7):1098-1107. doi: 10.1093/bioinformatics/bty715. Bioinformatics. 2019. PMID: 30169744

10

An Analysis of Natural T Cell Responses to Predicted Tumor Neoepitopes.

Bjerregaard AM, Nielsen M, Jurtz V, Barra CM, Hadrup SR, Szallasi Z, Eklund AC. Bjerregaard AM, et al. Front Immunol. 2017 Nov 15;8:1566. doi: 10.3389/fimmu.2017.01566. eCollection 2017. Front Immunol. 2017. PMID: 29187854 Free PMC article.

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