A deep learning approach to identify gene targets of a therapeutic for human splicing disorders

A deep learning approach to identify gene targets of a therapeutic for human splicing disorders

Drugs that modify RNA splicing are promising treatments for many genetic diseases. Here the authors show that deep learning strategies can predict drug targets, strongly supporting the use of in silico approaches to expand the therapeutic potential of drugs that modulate RNA splicing.

Bibliographic Details
Main Authors: Dadi Gao, Elisabetta Morini, Monica Salani, Aram J. Krauson, Anil Chekuri, Neeraj Sharma, Ashok Ragavendran, Serkan Erdin, Emily M. Logan, Wencheng Li, Amal Dakka, Jana Narasimhan, Xin Zhao, Nikolai Naryshkin, Christopher R. Trotta, Kerstin A. Effenberger, Matthew G. Woll, Vijayalakshmi Gabbeta, Gary Karp, Yong Yu, Graham Johnson, William D. Paquette, Garry R. Cutting, Michael E. Talkowski, Susan A. Slaugenhaupt
Format: Article
Language:English
Published: Nature Publishing Group 2021-06-01
Series:Nature Communications
Online Access:https://doi.org/10.1038/s41467-021-23663-2

Internet

https://doi.org/10.1038/s41467-021-23663-2

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