Single-Cell Techniques and Deep Learning in Predicting Drug Response.

Single-Cell Techniques and Deep Learning in Predicting Drug Response.
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单细胞技术和深度学习在药物反应预测中的应用

DOI:
10.1016/j.tips.2020.10.004
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发表时间:
2020-12
影响因子:
13.8
通讯作者:
Ma Q
Ma Q
中科院分区:
医学1区
文献类型:
--
作者:
Wu Z;Lawrence PJ;Ma A;Zhu J;Xu D;Ma Q

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与传统的批量测序分析相比,快速发展的单细胞测序分析产生了肿瘤亚群中基因组、转录组和表观基因组异质性的更全面的概况。此外,单细胞技术可以更彻底地研究肿瘤对药物暴露的反应。深度学习模型已经成功地从复杂的批量序列数据中提取特征来预测药物反应。在这里,我们回顾了与药物敏感性预测相关的单细胞技术和基于深度学习的方法的最新创新。我们相信,利用大量序列数据的洞察力,深度迁移学习将促进单细胞数据的应用,以训练卓越的基于深度学习的药物预测模型。
Rapidly developing single-cell sequencing analyses produce more comprehensive profiles of genomic, transcriptomic, and epigenomic heterogeneity present in tumor subpopulations than traditional bulk sequencing analyses. Moreover, single-cell techniques allow a tumor’s response to drug exposure to be more thoroughly investigated. Deep learning models have successfully extracted features from complex bulk sequence data to predict drug responses. Here, we review recent innovations in single-cell technologies and deep learning-based approaches related to drug sensitivity predictions. We believe that using insights from bulk sequence data, deep transfer learning would facilitate the application of single-cell data to train superior deep learning-based drug prediction models.
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