Journal
TRENDS IN PHARMACOLOGICAL SCIENCES
Volume 41, Issue 12, Pages 1050-1065Publisher
CELL PRESS
DOI: 10.1016/j.tips.2020.10.004
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Funding
- National Institute of General Medical Sciences of the National Institutes of Health [R35-GM126985, R01-GM131399, R01-DE025447]
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Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of the genomic, transcriptomic, and epigenomic heterogeneity of tumor subpopulations than do traditional bulk sequencing analyses. Moreover, single -cell techniques allow the response of a tumor to drug exposure to be more thoroughly investigated. Deep learning (DL) models have successfully extracted features from complex bulk sequence data to predict drug responses. We review recent innovations in single-cell technologies and DL-based approaches related to drug sensitivity predictions. We believe that, by using insights from bulk sequence data, deep transfer learning (DTL) can facilitate the use of single-cell data for training superior DL-based drug prediction models.
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