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  
20.04. How to read a scientific paper?  


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.
K. Rajan, J.-M. Hein, C. Steinbeck, and A. Zielesny, “Molecule Set Comparator (MSC): a CDK-based open rich‐client tool for molecule set similarity evaluations,” Journal of Cheminformatics, vol. 13, no. 1, p. 5, Feb. 2021, doi: 10.1186/s13321-021-00485-4.
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.
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.
B. J. Place, “Development of a Data Analysis Tool to Determine the Measurement Variability of Consensus Mass Spectra,” J. Am. Soc. Mass Spectrom., vol. 32, no. 3, pp. 707–715, Mar. 2021, doi: 10.1021/jasms.0c00423.
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.
T. Gerasimoska, M. Ljoncheva, and M. Simjanoska, “MSL-ST: Development of Mass Spectral Library Search Tool to Enhance Compound Identification,” Feb. 2021, doi: 10.5220/0010424101950203.
Y. Orlova, A. A. Gambardella, I. Kryven, K. Keune, and P. D. Iedema, “Generative Algorithm for Molecular Graphs Uncovers Products of Oil Oxidation,” J. Chem. Inf. Model., vol. 61, no. 3, pp. 1457–1469, Mar. 2021, doi: 10.1021/acs.jcim.0c01163.
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.
A. McCabe and A. R. Jones, “lcmsWorld: High-Performance 3D Visualization Software for Mass Spectrometry,” J. Proteome Res., vol. 20, no. 4, pp. 1981–1985, Apr. 2021, doi: 10.1021/acs.jproteome.0c00618.
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.
Y. You et al., “Unsupervised Reconstruction of Analyte-Specific Mass Spectra Based on Time-Domain Morphology with a Modified Cross-Correlation Approach,” Anal. Chem., vol. 93, no. 12, pp. 5009–5014, Mar. 2021, doi: 10.1021/acs.analchem.0c04396.
C. A. Krettler and G. G. Thallinger, “A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics,” Briefings in Bioinformatics, no. bbab073, Mar. 2021, doi: 10.1093/bib/bbab073.
A. Rutz et al., “Open Natural Products Research: Curation and Dissemination of Biological Occurrences of Chemical Structures through Wikidata,” Bioinformatics, preprint, Mar. 2021. doi: 10.1101/2021.02.28.433265.



Abstract Template (Abgabe 31.08.2021)