Currents in Bioinformatics

  • Onlineveranstaltung; Zoom-Meeting
  • Dienstag 16:15-17:45 Uhr
  • Beginn: 13.04.2021
  • Seminarleitung: Fleming Kretschmer

Diese Veranstaltung findet dieses Semester online statt. Teilnehmer erhalten den Einladungslink per E-mail über Friedolin.

Vorläufiger Ablaufplan

Date Paper Presenter and Backup
13.04 Organisatorisches, Besprechen der Themen  
27.04. [1]  
04.05. [10]  
11.05. [15]  
18.05. Schnelldurchlauf “Bioinformatics” Papers  
25.05. [5]  
01.06. [16]  
08.06. [17]  
15.06. [19], [20], ?  
22.06. [2]  
29.06. [4]  
06.07. Schnelldurchlauf  
13.07. [12]  


List of papers

P. A. Kreitzberg, M. Bern, Q. Shu, F. Yang, and O. Serang, “Alphabet Projection of Spectra,” J. Proteome Res., vol. 18, no. 9, pp. 3268–3281, Sep. 2019, doi: 10.1021/acs.jproteome.9b00216.
M. The and L. Käll, “Integrated Identification and Quantification Error Probabilities for Shotgun Proteomics * [S],” Molecular & Cellular Proteomics, vol. 18, no. 3, pp. 561–570, Mar. 2019, doi: 10.1074/mcp.RA118.001018.
belegt: W. Bittremieux, K. Laukens, W. S. Noble, and P. C. Dorrestein, “Large-scale tandem mass spectrum clustering using fast nearest neighbor searching,” bioRxiv, p. 2021.02.05.429957, Feb. 2021, doi: 10.1101/2021.02.05.429957.
belegt: F. Imrie, A. R. Bradley, and C. M. Deane, “Generating property-matched decoy molecules using deep learning,” Bioinformatics, no. btab080, Feb. 2021, doi: 10.1093/bioinformatics/btab080.
A. E. Blanchard, C. Stanley, and D. Bhowmik, “Using GANs with adaptive training data to search for new molecules,” Journal of Cheminformatics, vol. 13, no. 1, p. 14, Feb. 2021, doi: 10.1186/s13321-021-00494-3.
R. Huang et al., “Biological activity-based modeling identifies antiviral leads against SARS-CoV-2,” Nature Biotechnology, pp. 1–7, Feb. 2021, doi: 10.1038/s41587-021-00839-1.
K. Peters, G. Balcke, N. Kleinenkuhnen, H. Treutler, and S. Neumann, “Untargeted In Silico Compound Classification—A Novel Metabolomics Method to Assess the Chemodiversity in Bryophytes,” International Journal of Molecular Sciences, vol. 22, no. 6, Art. no. 6, Jan. 2021, doi: 10.3390/ijms22063251.
A. K. Jarmusch et al., “ReDU: a framework to find and reanalyze public mass spectrometry data,” Nature Methods, vol. 17, no. 9, Art. no. 9, Sep. 2020, doi: 10.1038/s41592-020-0916-7.
D. Petras et al., “Chemical Proportionality within Molecular Networks,” Apr. 2021, doi: 10.26434/chemrxiv.14396105.v1.
F. Huber, S. van der Burg, J. J. J. van der Hooft, and L. Ridder, “MS2DeepScore – a novel deep learning similarity measure for mass fragmentation spectrum comparisons,” bioRxiv, p. 2021.04.18.440324, Apr. 2021, doi: 10.1101/2021.04.18.440324.
M. Wang et al., “RobNorm: model-based robust normalization method for labeled quantitative mass spectrometry proteomics data,” Bioinformatics, vol. 37, no. 6, pp. 815–821, Mar. 2021, doi: 10.1093/bioinformatics/btaa904.
A. Borzou and R. G. Sadygov, “A novel estimator of the interaction matrix in Graphical Gaussian Model of omics data using the entropy of non-equilibrium systems,” Bioinformatics, vol. 37, no. 6, pp. 837–844, Mar. 2021, doi: 10.1093/bioinformatics/btaa894.
S. Picart-Armada, W. K. Thompson, A. Buil, and A. Perera-Lluna, “The effect of statistical normalization on network propagation scores,” Bioinformatics, vol. 37, no. 6, pp. 845–852, Mar. 2021, doi: 10.1093/bioinformatics/btaa896.
C. Drury et al., “Intrapopulation adaptive variance supports selective breeding in a reef-building coral,” bioRxiv, p. 2021.05.21.445206, May 2021, doi: 10.1101/2021.05.21.445206.
G. K. Reder et al., “Supervised topic modeling for predicting molecular substructure from mass spectrometry,” F1000Res, vol. 10, p. 403, May 2021, doi: 10.12688/f1000research.52549.1.



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