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.
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Nature Portfolio
2021
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oai:doaj.org-article:a1488d5827774183bb159243db93b8152021-12-02T14:58:13ZA deep learning approach to identify gene targets of a therapeutic for human splicing disorders10.1038/s41467-021-23663-22041-1723https://doaj.org/article/a1488d5827774183bb159243db93b8152021-06-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-23663-2https://doaj.org/toc/2041-1723Drugs 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.Dadi GaoElisabetta MoriniMonica SalaniAram J. KrausonAnil ChekuriNeeraj SharmaAshok RagavendranSerkan ErdinEmily M. LoganWencheng LiAmal DakkaJana NarasimhanXin ZhaoNikolai NaryshkinChristopher R. TrottaKerstin A. EffenbergerMatthew G. WollVijayalakshmi GabbetaGary KarpYong YuGraham JohnsonWilliam D. PaquetteGarry R. CuttingMichael E. TalkowskiSusan A. SlaugenhauptNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-15 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
Science Q |
| spellingShingle |
Science Q 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 A deep learning approach to identify gene targets of a therapeutic for human splicing disorders |
| description |
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. |
| format |
article |
| author |
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 |
| author_facet |
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 |
| author_sort |
Dadi Gao |
| title |
A deep learning approach to identify gene targets of a therapeutic for human splicing disorders |
| title_short |
A deep learning approach to identify gene targets of a therapeutic for human splicing disorders |
| title_full |
A deep learning approach to identify gene targets of a therapeutic for human splicing disorders |
| title_fullStr |
A deep learning approach to identify gene targets of a therapeutic for human splicing disorders |
| title_full_unstemmed |
A deep learning approach to identify gene targets of a therapeutic for human splicing disorders |
| title_sort |
deep learning approach to identify gene targets of a therapeutic for human splicing disorders |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/a1488d5827774183bb159243db93b815 |
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