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Orlando, Gabriele, Raimondi, Daniele, F. Vranken, Wim (2019) Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index. Nature Communications, 10. doi:10.1038/s41467-019-10322-w

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Reference TypeJournal (article/letter/editorial)
TitleAuto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index
JournalNature Communications
AuthorsOrlando, GabrieleAuthor
Raimondi, DanieleAuthor
F. Vranken, WimAuthor
Year2019 (December)Volume10
PublisherSpringer Science and Business Media LLC
DOIdoi:10.1038/s41467-019-10322-wSearch in ResearchGate
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Mindat Ref. ID6099666Long-form Identifiermindat:1:5:6099666:2
GUID0
Full ReferenceOrlando, Gabriele, Raimondi, Daniele, F. Vranken, Wim (2019) Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index. Nature Communications, 10. doi:10.1038/s41467-019-10322-w
Plain TextOrlando, Gabriele, Raimondi, Daniele, F. Vranken, Wim (2019) Auto-encoding NMR chemical shifts from their native vector space to a residue-level biophysical index. Nature Communications, 10. doi:10.1038/s41467-019-10322-w
In(2019) Nature Communications Vol. 10. Springer Science and Business Media LLC

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Raimondi, Daniele, Orlando, Gabriele, Vranken, Wim F., Moreau, Yves (2019) Exploring the limitations of biophysical propensity scales coupled with machine learning for protein sequence analysis. Scientific Reports, 9. doi:10.1038/s41598-019-53324-w
Raimondi, Daniele, Orlando, Gabriele, Pancsa, Rita, Khan, Taushif, Vranken, Wim F. (2019) Author Correction: Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins. Scientific Reports, 9. doi:10.1038/s41598-019-48219-9
Raimondi, Daniele, Orlando, Gabriele, Pancsa, Rita, Khan, Taushif, Vranken, Wim F. (2017) Exploring the Sequence-based Prediction of Folding Initiation Sites in Proteins. Scientific Reports, 7. doi:10.1038/s41598-017-08366-3
Raimondi, Daniele, Orlando, Gabriele, Tabaro, Francesco, Lenaerts, Tom, Rooman, Marianne, Moreau, Yves, Vranken, Wim F. (2018) Large-scale in-silico statistical mutagenesis analysis sheds light on the deleteriousness landscape of the human proteome. Scientific Reports, 8. doi:10.1038/s41598-018-34959-7
Orlando, Gabriele, Raimondi, Daniele, Codicè, Francesco, Tabaro, Francesco, Vranken, Wim (2022) Prediction of Disordered Regions in Proteins with Recurrent Neural Networks and Protein Dynamics. Journal of Molecular Biology, 434 (12) 167579 doi:10.1016/j.jmb.2022.167579
Orlando, Gabriele, Raimondi, Daniele, Duran-Romaña, Ramon, Moreau, Yves, Schymkowitz, Joost, Rousseau, Frederic (2022) PyUUL provides an interface between biological structures and deep learning algorithms. Nature Communications, 13 (1) doi:10.1038/s41467-022-28327-3
Orlando, G., Raimondi, D., Vranken, W. F. (2016) Observation selection bias in contact prediction and its implications for structural bioinformatics. Scientific Reports, 6. doi:10.1038/srep36679
Hoffmann, Samuel, Irl, Severin D. H., Beierkuhnlein, Carl (2019) Predicted climate shifts within terrestrial protected areas worldwide. Nature Communications, 10. doi:10.1038/s41467-019-12603-w
Cilia, Elisa, Pancsa, Rita, Tompa, Peter, Lenaerts, Tom, Vranken, Wim F. (2013) From protein sequence to dynamics and disorder with DynaMine. Nature Communications, 4. doi:10.1038/ncomms3741
Hou, Zengqian, Wang, Rui (2019) Fingerprinting metal transfer from mantle. Nature Communications, 10. doi:10.1038/s41467-019-11445-w
Loi, Marisa, Raimondi, Andrea, Morone, Diego, Molinari, Maurizio (2019) ESCRT-III-driven piecemeal micro-ER-phagy remodels the ER during recovery from ER stress. Nature Communications, 10. doi:10.1038/s41467-019-12991-z
 
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