Currents in Bioinformatics

  • Onlineveranstaltung; Zoom-Meeting
  • Dienstag 16:15-17:45 Uhr
  • Beginn: 03.11.2020
  • 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
3.11. Organisatorisches, Besprechen der Themen  
10.11. How to read a scientific paper? Martin
1.12. Nr. 12  
8.12. Nr. 14  
15.12. Nr. 8  
5.1. Nr. 11  
12.1. Nr. 10  
19.1. Nr. 1  
2.2. Nr. 7  
9.2. Nr. 16  

List of papers:

  1. Bach, E. et al. (2020) ‘Probabilistic framework for integration of mass spectrum and retention time information in small molecule identification’, Bioinformatics, (btaa998). doi: 10.1093/bioinformatics/btaa998.
  2. Behr, M. et al. (2020) ‘Testing for dependence on tree structures’, Proceedings of the National Academy of Sciences, 117(18), pp. 9787–9792. doi: 10.1073/pnas.1912957117.
  3. Bento, A. P. et al. (2020) ‘An open source chemical structure curation pipeline using RDKit’, Journal of Cheminformatics, 12(1), p. 51. doi: 10.1186/s13321-020-00456-1.
  4. Chen, T., Kornblith, S., Norouzi, M., et al. (2020) ‘A Simple Framework for Contrastive Learning of Visual Representations’, arXiv:2002.05709 [cs, stat]. Available at: (Accessed: 30 October 2020).
  5. Chen, T., Kornblith, S., Swersky, K., et al. (2020) ‘Big Self-Supervised Models are Strong Semi-Supervised Learners’, arXiv:2006.10029 [cs, stat]. Available at: (Accessed: 30 October 2020).
  6. Ciach, M. A. et al. (2020) ‘Masserstein: linear regression of mass spectra by optimal transport’, Rapid Communications in Mass Spectrometry, n/a(n/a). doi: 10.1002/rcm.8956.
  7. Fraser, D. D. et al. (2020) ‘Metabolomics Profiling of Critically Ill Coronavirus Disease 2019 Patients: Identification of Diagnostic and Prognostic Biomarkers’, Critical Care Explorations, 2(10), p. e0272. doi: 10.1097/CCE.0000000000000272.
  8. Garriga, E. et al. (2019) ‘Large multiple sequence alignments with a root-to-leaf regressive method’, Nature Biotechnology, 37(12), pp. 1466–1470. doi: 10.1038/s41587-019-0333-6.
  9. Köppel, M. et al. (2020) ‘Pairwise Learning to Rank by Neural Networks Revisited: Reconstruction, Theoretical Analysis and Practical Performance’, in Brefeld, U. et al. (eds) Machine Learning and Knowledge Discovery in Databases. Cham: Springer International Publishing (Lecture Notes in Computer Science), pp. 237–252. doi: 10.1007/978-3-030-46133-1_15.
  10. Marco-Sola, S. et al. (2020) ‘Fast gap-affine pairwise alignment using the wavefront algorithm’, Bioinformatics. doi: 10.1093/bioinformatics/btaa777.
  11. Mohimani, H. et al. (2020) MolDiscovery: Learning Mass Spectrometry Fragmentation of Small Molecules. preprint. In Review. doi: 10.21203/
  12. Roach, T. N. F. et al. (2020) ‘A multiomic analysis of in situ coral–turf algal interactions’, Proceedings of the National Academy of Sciences, 117(24), pp. 13588–13595. doi: 10.1073/pnas.1915455117.
    1. Kopczynski, D. et al. (2020) ‘Goslin: A Grammar of Succinct Lipid Nomenclature’, Analytical Chemistry, 92(16), pp. 10957–10960. doi: 10.1021/acs.analchem.0c01690.
    2. Pauling, J. K. et al. (2017) ‘Proposal for a common nomenclature for fragment ions in mass spectra of lipids’, PLOS ONE, 12(11), p. e0188394. doi: 10.1371/journal.pone.0188394.
  14. Rutz, A. et al. (2019) ‘Taxonomically Informed Scoring Enhances Confidence in Natural Products Annotation’. doi: 10.3389/fpls.2019.01329
  15. Schmidt, R. et al. (2020) ‘Disconnected Maximum Common Substructures under Constraints’. doi: 10.1021/acs.jcim.0c00741
  16. Skinnider, M. A. et al. (2021) ‘Deep Generative Models Enable Navigation in Sparsely Populated Chemical Space’. doi: 10.26434/chemrxiv.13638347.v1.


  1. Baker, C. M. et al. (2020) ‘Tautomer Standardization in Chemical Databases: Deriving Business Rules from Quantum Chemistry’, Journal of Chemical Information and Modeling, 60(8), pp. 3781–3791. doi: 10.1021/acs.jcim.0c00232.
  2. Hirschfeld, L. et al. (2020) ‘Uncertainty Quantification Using Neural Networks for Molecular Property Prediction’, Journal of Chemical Information and Modeling, 60(8), pp. 3770–3780. doi: 10.1021/acs.jcim.0c00502.
  3. Jin, H., Mitchell, J. M. and Moseley, H. N. B. (2020) ‘Atom Identifiers Generated by a Neighborhood-Specific Graph Coloring Method Enable Compound Harmonization across Metabolic Databases’, Metabolites, 10(9), p. 368. doi: 10.3390/metabo10090368.
  4. Kim, H. et al. (2020) ‘NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products’. doi: 10.26434/chemrxiv.12885494.v1.

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