Currents in Bioinformatics

  • Seminarleitung: Fleming Kretschmer / Markus Fleischauer
  • Seminarort: SR 3423 EAP2
  • Dienstag 16:15-17:45 Uhr
  • Beginn: 12.04.2022
12.04.2022 Organisatorisches
19.04.2022 Wie liest man ein Paper?
26.04.2022 [9]
03.05.2022 [2]
10.05.2022 [8]
17.05.2022 [6]
24.05.2022 [12]
31.05.2022 [7]
07.06.2022 [10]
14.06.2022 [4]
21.06.2022 entfällt
28.06.2022 [3]
05.07.2022 [16]
12.07.2022 [13], [15]

Paperliste

A-List

[1]
R. Mercado et al., “Graph networks for molecular design,” Mach. Learn.: Sci. Technol., vol. 2, no. 2, p. 025023, Mar. 2021, doi: 10.1088/2632-2153/abcf91.
[2]
T. P. Quinn, “Stool Studies Don’t Pass the Sniff Test: A Systematic Review of Human Gut Microbiome Research Suggests Widespread Misuse of Machine Learning,” arXiv:2107.03611 [q-bio], Jul. 2021, Accessed: Apr. 12, 2022. [Online]. Available: http://arxiv.org/abs/2107.03611
[3]
Y. Ma et al., “Identification of antimicrobial peptides from the human gut microbiome using deep learning,” Nat Biotechnol, pp. 1–11, Mar. 2022, doi: 10.1038/s41587-022-01226-0.
[4]
“Causal integration of multi-omics data with prior knowledge to generate mechanistic hypotheses,” Molecular Systems Biology, vol. 17, no. 1, p. e9730, Jan. 2021, doi: 10.15252/msb.20209730.


Hier 1/2 Kapitel:
[5]
M. M. Bronstein, J. Bruna, T. Cohen, and P. Veličković, “Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges,” arXiv:2104.13478 [cs, stat], May 2021, Accessed: Apr. 12, 2022. [Online]. Available: http://arxiv.org/abs/2104.13478

[6]
“Heterogeneous multimeric metabolite ion species observed in LC-MS based metabolomics data sets | bioRxiv.” https://www.biorxiv.org/content/10.1101/2022.03.15.484295v1 (accessed Apr. 12, 2022).
[7]
O. Alka, P. Shanthamoorthy, M. Witting, K. Kleigrewe, O. Kohlbacher, and H. L. Röst, “DIAMetAlyzer allows automated false-discovery rate-controlled analysis for data-independent acquisition in metabolomics,” Nat Commun, vol. 13, no. 1, Art. no. 1, Mar. 2022, doi: 10.1038/s41467-022-29006-z.
[8]
C. Noecker, A. Eng, E. Muller, and E. Borenstein, “MIMOSA2: a metabolic network-based tool for inferring mechanism-supported relationships in microbiome‐metabolome data,” Bioinformatics, vol. 38, no. 6, pp. 1615–1623, Mar. 2022, doi: 10.1093/bioinformatics/btac003.
[9]
K. J. Abram and D. McCloskey, “A Comprehensive Evaluation of Metabolomics Data Preprocessing Methods for Deep Learning,” Metabolites, vol. 12, no. 3, Art. no. 3, Mar. 2022, doi: 10.3390/metabo12030202.
 
B-List
 
[10]
A. E. Blanchard, C. Stanley, and D. Bhowmik, “Using GANs with adaptive training data to search for new molecules,” J Cheminform, vol. 13, no. 1, Art. no. 1, Dec. 2021, doi: 10.1186/s13321-021-00494-3.
[11]
X. Li and D. Fourches, “SMILES Pair Encoding: A Data-Driven Substructure Tokenization Algorithm for Deep Learning,” J. Chem. Inf. Model., vol. 61, no. 4, pp. 1560–1569, Apr. 2021, doi: 10.1021/acs.jcim.0c01127.
[12]
“Learning the protein language: Evolution, structure, and function – ScienceDirect.” https://www.sciencedirect.com/science/article/pii/S2405471221002039?via%3Dihub (accessed Apr. 12, 2022).
[13]
J. J. R. Louwen and J. J. J. van der Hooft, “Comprehensive Large-Scale Integrative Analysis of Omics Data To Accelerate Specialized Metabolite Discovery,” mSystems, vol. 6, no. 4, pp. e00726-21, doi: 10.1128/mSystems.00726-21.
[14]
B. J. Place et al., “An Introduction to the Benchmarking and Publications for Non-Targeted Analysis Working Group,” Anal. Chem., vol. 93, no. 49, pp. 16289–16296, Dec. 2021, doi: 10.1021/acs.analchem.1c02660.
[15]
G. Tamasco, R. R. da Silva, and R. Silva-Rocha, “ChiMera: An easy to use pipeline for Bacterial Genome Based Metabolic Network Reconstruction, Evaluation and Visualization.” bioRxiv, p. 2021.11.30.470608, Dec. 01, 2021. doi: 10.1101/2021.11.30.470608.
[16]
B. D. McKay, M. A. Yirik, and C. Steinbeck, “Surge: a fast open-source chemical graph generator,” J Cheminform, vol. 14, no. 1, p. 24, Apr. 2022, doi: 10.1186/s13321-022-00604-9.

(_): belegt
Abgabe Abstract: 31.08.2022, Template