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

Autor: 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
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Nature Communications, Vol 12, Iss 1, Pp 1-15 (2021)
Druh dokumentu: article
ISSN: 2041-1723
DOI: 10.1038/s41467-021-23663-2
Popis: 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.
Databáze: Directory of Open Access Journals
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