Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
Dokumenter
- Fulltext
Forlagets udgivne version, 3,86 MB, PDF-dokument
Originalsprog | Engelsk |
---|---|
Artikelnummer | 180 |
Tidsskrift | Genome Biology |
Vol/bind | 24 |
Udgave nummer | 1 |
Antal sider | 37 |
ISSN | 1474-7596 |
DOI | |
Status | Udgivet - 2023 |
Bibliografisk note
Funding Information:
Open Access funding enabled and organized by Projekt DEAL. This work was supported by the Helmholtz Association under the joint research school “Munich School for Data Science (MUDS)” to M.H., N.W., J.G. and A.M., the Deutsche Forschungsgemeinschaft (SFB/TR501 84 TP C01) to A.M. and L.M. and (SFB/Transregio TRR267) to J.G.; O.W.’s work was funded in part by the Novo Nordisk Foundation through the Center for Basic Machine Learning Research in Life Science (NNF20OC0062606). O.W. further acknowledges support from the Pioneer Centre for AI, DNRF grant number P1; K.K.’s and J.U.’s work was funded by the European Union’s Horizon 2020 research and innovation program (835300-RNPdynamics). K.K. and J.U. further acknowledge support from The Francis Crick Institute, which receives its core funding from Cancer Research UK (FC001110), the UK Medical Research Council (FC001110), and the Wellcome Trust (FC001110).
Publisher Copyright:
© 2023, BioMed Central Ltd., part of Springer Nature.
ID: 362744991