Currents in Bioinformatics

  • Dienstag 16:15 – 17:45 Uhr; SR 3423 EAP2
  • Beginn: 18.10.2022
  • Seminarleitung: Fleming Kretschmer

Vorläufiger Ablaufplan

Date Paper Presenter and Backup
18.10. Organisatorisches, Besprechen der Themen  
25.10. Wie liest man Paper?  
01.11. [5]  
08.11. [16]  
15.11. [17], evtl. [18]  
22.11. [8]  
29.11. [9]  
06.12. [2]  

Vorläufige Paper-Liste

K. Mildau et al., “Homologue series detection and management in LC-MS data with homologueDiscoverer,” Bioinformatics, p. btac647, Sep. 2022, doi: 10.1093/bioinformatics/btac647.
C. Simon et al., “Mass Difference Matching Unfolds Hidden Molecular Structures of Dissolved Organic Matter,” Environ. Sci. Technol., vol. 56, no. 15, pp. 11027–11040, Aug. 2022, doi: 10.1021/acs.est.2c01332.
L. Tian et al., “Metapone: a Bioconductor package for joint pathway testing for untargeted metabolomics data,” Bioinformatics, vol. 38, no. 14, pp. 3662–3664, Jul. 2022, doi: 10.1093/bioinformatics/btac364.
B. Peng et al., “LipidCreator workbench to probe the lipidomic landscape,” Nat Commun, vol. 11, no. 1, Art. no. 1, Apr. 2020, doi: 10.1038/s41467-020-15960-z.
G. Voronov, R. Lightheart, J. Davison, C. A. Krettler, D. Healey, and T. Butler, “Multi-scale Sinusoidal Embeddings Enable Learning on High Resolution Mass Spectrometry Data.” arXiv, Jul. 06, 2022. doi: 10.48550/arXiv.2207.02980.
J. M. Gauglitz et al., “Enhancing untargeted metabolomics using metadata-based source annotation,” Nat Biotechnol, pp. 1–6, Jul. 2022, doi: 10.1038/s41587-022-01368-1.
H. K. Yu and H. C. Yu, “Powerful molecule generation with simple ConvNet,” Bioinformatics, vol. 38, no. 13, pp. 3438–3443, Jul. 2022, doi: 10.1093/bioinformatics/btac332.
Q. Xie, M.-T. Luong, E. Hovy, and Q. V. Le, “Self-training with Noisy Student improves ImageNet classification.” arXiv, Jun. 19, 2020. doi: 10.48550/arXiv.1911.04252.
F. Malinka, A. Zareie, J. Prochazka, R. Sedlacek, and V. Novosadova, “Batch alignment via retention orders for preprocessing large-scale multi-batch LC-MS experiments,” Bioinformatics, vol. 38, no. 15, pp. 3759–3767, Aug. 2022, doi: 10.1093/bioinformatics/btac407.
R. Wang et al., “Global stable-isotope tracing metabolomics reveals system-wide metabolic alternations in aging Drosophila,” Nat Commun, vol. 13, no. 1, Art. no. 1, Jun. 2022, doi: 10.1038/s41467-022-31268-6.
Y. Du, X. Guo, Y. Wang, A. Shehu, and L. Zhao, “Small molecule generation via disentangled representation learning,” Bioinformatics, vol. 38, no. 12, pp. 3200–3208, Jun. 2022, doi: 10.1093/bioinformatics/btac296.
S. Jahagirdar and E. Saccenti, “Evaluation of Single Sample Network Inference Methods for Metabolomics-Based Systems Medicine,” J. Proteome Res., vol. 20, no. 1, pp. 932–949, Jan. 2021, doi: 10.1021/acs.jproteome.0c00696.
“Good Practices and Recommendations for Using and Benchmarking Computational Metabolomics Metabolite Annotation Tools,” May 24, 2022, doi: 10.21203/
M. M. Bronstein, J. Bruna, T. Cohen, and P. Veličković, “Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges.” arXiv, May 02, 2021. doi: 10.48550/arXiv.2104.13478. (Hier ein oder zwei Kapitel)
P. Peets, W.-C. Wang, M. MacLeod, M. Breitholtz, J. W. Martin, and A. Kruve, “MS2Tox Machine Learning Tool for Predicting the Ecotoxicity of Unidentified Chemicals in Water by Nontarget LC-HRMS,” Environ. Sci. Technol., Oct. 2022, doi: 10.1021/acs.est.2c02536.
I. Koester et al., “Illuminating the dark metabolome of Pseudo-nitzschia-microbiome associations,” Environmental Microbiology, doi: 10.1111/1462-2920.16242.
E. E. Kontou et al., “UmetaFlow: An untargeted metabolomics workflow for high-throughput data processing and analysis,” Oct. 2022, doi: 10.26434/chemrxiv-2022-z0t4g-v2.

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