Learning the sequence determinants of alternative splicing from millions of random sequences.

By Alexander B. Rosenberg, Rupali Patwardhan, Jay Shendure, Georg Seelig. 2015

261 citations recorded by the source corpus.

Subfields: Molecular Biology

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In Molecular Biology

1. Sergey I. Nikolenko

Citations in the corpus

• Deep learning of the regulatory grammar of yeast 5′ untranslated regions from 500,000 random sequences (2017, unknown): “Our study of alternative splicing corroborates the idea that highly predictive biological models can be learned from fully degenerate sequences (Rosenberg et al. 2015).”
• Synthetic Data for Deep Learning (2019, background): “[502] study alternative RNA splicing, in particular the functional effects of genetic variation on the molecular phenotypes through alternative splicing.”