Currents in Bioinformatics

  • Dienstag 16:15 – 17:45 Uhr; SR 3423 EAP2
  • Beginn: 19.10.2021
  • Seminarleitung: Fleming Kretschmer

Vorläufiger Ablaufplan

Date Paper Presenter and Backup
19.10. Organisatorisches, Besprechen der Themen  
26.10. [6]  
2.11. [8] (, [9])  
9.11. [7]  
16.11. [10] (, [11])  
23.11. [12], [13]  
30.11. [14]  
7.12. [15], ([16-18])  
4.1. [1]  
11.1. [19], [20]  
25.1. [21]  
1.2. [22], [23], [24]  
8.2. [3]  

List of papers:

alles vorläufig

B. Sanchez-Lengeling, E. Reif, A. Pearce, and A. B. Wiltschko, “A Gentle Introduction to Graph Neural Networks,” Distill, vol. 6, no. 9, p. e33, Sep. 2021, doi: 10.23915/distill.00033.)
A. D. Shrivastava, N. Swainston, S. Samanta, I. Roberts, M. W. Muelas, and D. B. Kell, “MassGenie: a transformer-based deep learning method for identifying small molecules from their mass spectra,” Bioinformatics, preprint, Jun. 2021. doi: 10.1101/2021.06.25.449969.
“Bi-modal Variational Autoencoders for Metabolite Identification Using Tandem Mass Spectrometry | bioRxiv.” (accessed Oct. 19, 2021).
E. Litsa, V. Chenthamarakshan, P. Das, and L. Kavraki, “Spec2Mol: An end-to-end deep learning framework for translating MS/MS Spectra to de-novo molecules,” Sep. 2021, doi: 10.33774/chemrxiv-2021-6rdh6.
T. F. Leao et al., “A supervised fingerprint-based strategy to connect natural product mass spectrometry fragmentation data to their biosynthetic gene clusters,” Oct. 2021. doi: 10.1101/2021.10.05.463235.
J. Li and X. Jiang, “Mol-BERT: An Effective Molecular Representation with BERT for Molecular Property Prediction,” Wireless Communications and Mobile Computing, vol. 2021, pp. 1–7, Sep. 2021, doi: 10.1155/2021/7181815.
J. Jumper et al., “Highly accurate protein structure prediction with AlphaFold,” Nature, vol. 596, no. 7873, pp. 583–589, Aug. 2021, doi: 10.1038/s41586-021-03819-2.
P. Dorrestein et al., “A Synthesis-Based Reverse Metabolomics Approach for the Discovery of Chemical Structures from Humans and Animals.” Oct. 29, 2021. doi: 10.21203/
J. Ding et al., “A metabolome atlas of the aging mouse brain,” Nat Commun, vol. 12, no. 1, p. 6021, Oct. 2021, doi: 10.1038/s41467-021-26310-y.
R. Giné et al., “HERMES: a molecular-formula-oriented method to target the metabolome,” Nat Methods, vol. 18, no. 11, pp. 1370–1376, Nov. 2021, doi: 10.1038/s41592-021-01307-z.
A. Young, B. Wang, and H. Röst, “MassFormer: Tandem Mass Spectrum Prediction with Graph Transformers,” arXiv:2111.04824 [cs, q-bio], Nov. 2021, Accessed: Nov. 11, 2021. [Online]. Available:
A. Kensert, R. Bouwmeester, K. Efthymiadis, P. Van Broeck, G. Desmet, and D. Cabooter, “Graph Convolutional Networks for Improved Prediction and Interpretability of Chromatographic Retention Data,” Anal. Chem., Nov. 2021, doi: 10.1021/acs.analchem.1c02988.
G. Xing, V. Sresht, Z. Sun, Y. Shi, and M. F. Clasquin, “Coupling Mixed Mode Chromatography/ESI Negative MS Detection with Message-Passing Neural Network Modeling for Enhanced Metabolome Coverage and Structural Identification,” Metabolites, vol. 11, no. 11, Art. no. 11, Nov. 2021, doi: 10.3390/metabo11110772.
L. Chen et al., “Metabolite discovery through global annotation of untargeted metabolomics data,” Nat Methods, vol. 18, no. 11, pp. 1377–1385, Nov. 2021, doi: 10.1038/s41592-021-01303-3.
“SLAW: A Scalable and Self-Optimizing Processing Workflow for Untargeted LC-MS | Analytical Chemistry.” (accessed Dec. 02, 2021).
S. A. Jarmusch, J. J. J. van der Hooft, P. C. Dorrestein, and A. K. Jarmusch, “Advancements in capturing and mining mass spectrometry data are transforming natural products research,” Natural Product Reports, vol. 38, no. 11, pp. 2066–2082, 2021, doi: 10.1039/D1NP00040C.
L. U. Kurt et al., “Characterizing protein conformers by cross-linking mass spectrometry and pattern recognition,” Bioinformatics, vol. 37, no. 18, pp. 3035–3037, Sep. 2021, doi: 10.1093/bioinformatics/btab149.
A. Bauermeister, H. Mannochio-Russo, L. V. Costa-Lotufo, A. K. Jarmusch, and P. C. Dorrestein, “Mass spectrometry-based metabolomics in microbiome investigations,” Nat Rev Microbiol, pp. 1–18, Sep. 2021, doi: 10.1038/s41579-021-00621-9.
D. Chicco, N. Tötsch, and G. Jurman, “The Matthews correlation coefficient (MCC) is more reliable than balanced accuracy, bookmaker informedness, and markedness in two-class confusion matrix evaluation,” BioData Mining, vol. 14, no. 1, Art. no. 1, Dec. 2021, doi: 10.1186/s13040-021-00244-z.
Q. Zhu, “On the performance of Matthews correlation coefficient (MCC) for imbalanced dataset,” Pattern Recognition Letters, vol. 136, pp. 71–80, Aug. 2020, doi: 10.1016/j.patrec.2020.03.030.
G. H. Eldjárn et al., “Ranking microbial metabolomic and genomic links in the NPLinker framework using complementary scoring functions,” PLOS Computational Biology, vol. 17, no. 5, p. e1008920, May 2021, doi: 10.1371/journal.pcbi.1008920.
C. Wieder et al., “Pathway analysis in metabolomics: Recommendations for the use of over-representation analysis,” PLOS Computational Biology, vol. 17, no. 9, p. e1009105, Sep. 2021, doi: 10.1371/journal.pcbi.1009105.
B. J. Blaise et al., “Statistical analysis in metabolic phenotyping,” Nat Protoc, vol. 16, no. 9, pp. 4299–4326, Sep. 2021, doi: 10.1038/s41596-021-00579-1.
P. Khatri, M. Sirota, and A. J. Butte, “Ten Years of Pathway Analysis: Current Approaches and Outstanding Challenges,” PLOS Computational Biology, vol. 8, no. 2, p. e1002375, Feb. 2012, doi: 10.1371/journal.pcbi.1002375.

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