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Hannes L. Röst
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- affiliation: University of Toronto, Canada
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2020 – today
- 2024
- [j7]Adamo Young, Hannes L. Röst
, Bo Wang
Tandem mass spectrum prediction for small molecules using graph transformers. Nat. Mac. Intell. 6(4): 404-416 (2024) - [i4]Adamo Young, Fei Wang, David S. Wishart, Bo Wang, Hannes L. Röst, Russ Greiner:
FraGNNet: A Deep Probabilistic Model for Mass Spectrum Prediction. CoRR abs/2404.02360 (2024) - 2021
- [j6]Felix Hanau
mspack: efficient lossless and lossy mass spectrometry data compression. Bioinform. 37(21): 3923-3925 (2021) - [i3]Adamo Young, Bo Wang, Hannes L. Röst:
MassFormer: Tandem Mass Spectrum Prediction with Graph Transformers. CoRR abs/2111.04824 (2021) - 2020
- [i2]Leon L. Xu, Hannes L. Röst:
Peak Detection On Data Independent Acquisition Mass Spectrometry Data With Semisupervised Convolutional Transformers. CoRR abs/2010.13841 (2020)
2010 – 2019
- 2018
- [i1]Björn A. Grüning, Olivier Sallou, Pablo A. Moreno, Felipe da Veiga Leprevost, Hervé Ménager, Dan Søndergaard, Hannes L. Röst, Timo Sachsenberg, Brian O'Connor, Fábio Madeira, Victoria Dominguez Del Angel, Michael R. Crusoe, Susheel Varma, Daniel J. Blankenberg, Rafael C. Jiménez, Daniel Blankenberg, Yasset Pérez-Riverol:
Recommendations for the packaging and containerizing of bioinformatics software. F1000Research 7: 742 (2018) - 2017
- [j5]Felipe da Veiga Leprevost
BioContainers: an open-source and community-driven framework for software standardization. Bioinform. 33(16): 2580-2582 (2017) - 2016
- [p1]Pedro Navarro, Marco Trevisan-Herraz, Hannes L. Röst
Chapter 10. Data Analysis for Data Independent Acquisition. Proteome Informatics 2016: 200-228 - 2015
- [j4]Johan Teleman, Hannes L. Röst
DIANA - algorithmic improvements for analysis of data-independent acquisition MS data. Bioinform. 31(4): 555-562 (2015) - [j3]Hannes L. Röst
Efficient visualization of high-throughput targeted proteomics experiments: TAPIR. Bioinform. 31(14): 2415-2417 (2015) - [j2]Lin-Yang Cheng, Yansheng Liu, Ching-Yun Chang, Hannes L. Röst
Statistical elimination of spectral features with large between-run variation enhances quantitative protein-level conclusions in experiments with data-independent spectral acquisition. BMC Bioinform. 16(S-2): A4 (2015) - 2014
- [j1]George Rosenberger
aLFQ: an R-package for estimating absolute protein quantities from label-free LC-MS/MS proteomics data. Bioinform. 30(17): 2511-2513 (2014)
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