Funder
Deutsche Forschungsgemeinschaft
Bundesministerium für Bildung und Forschung
Munich School for Data Science
Publisher
Springer Science and Business Media LLC
Reference65 articles.
1. Zappala, Z. & Montgomery, S. B. Non-coding loss-of-function variation in human genomes. Hum. Hered. 81, 78–87 (2016).
2. Jaganathan, K. et al. Predicting splicing from primary sequence with deep learning. Cell 176, 535–548.e24 (2019).
3. Cheng, J. et al. MMSplice: modular modeling improves the predictions of genetic variant effects on splicing. Genome Biol. 20, 48 (2019).
4. Yeo, G. & Burge, C. B. Maximum entropy modeling of short sequence motifs with applications to RNA splicing signals. J. Comput. Biol. 11, 377–394 (2004).
5. Rosenberg, A. B., Patwardhan, R. P., Shendure, J. & Seelig, G. Learning the sequence determinants of alternative splicing from millions of random sequences. Cell 163, 698–711 (2015).
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