Kalinin Alexandr A, Higgins Gerald A, Reamaroon Narathip, Soroushmehr Sayedmohammadreza, Allyn-Feuer Ari, Dinov Ivo D, Najarian Kayvan, Athey Brian D
Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, MI 48109, USA.
Statistics Online Computational Resource (SOCR), University of Michigan School of Nursing, Ann Arbor, MI 48109, USA.
Pharmacogenomics. 2018 May;19(7):629-650. doi: 10.2217/pgs-2018-0008. Epub 2018 Apr 26.
This Perspective provides examples of current and future applications of deep learning in pharmacogenomics, including: identification of novel regulatory variants located in noncoding domains of the genome and their function as applied to pharmacoepigenomics; patient stratification from medical records; and the mechanistic prediction of drug response, targets and their interactions. Deep learning encapsulates a family of machine learning algorithms that has transformed many important subfields of artificial intelligence over the last decade, and has demonstrated breakthrough performance improvements on a wide range of tasks in biomedicine. We anticipate that in the future, deep learning will be widely used to predict personalized drug response and optimize medication selection and dosing, using knowledge extracted from large and complex molecular, epidemiological, clinical and demographic datasets.
本观点文章提供了深度学习在药物基因组学中当前和未来应用的实例，包括：识别位于基因组非编码区域的新型调控变异及其在药物表观基因组学中的作用；从医疗记录中进行患者分层；以及药物反应、靶点及其相互作用的机制预测。深度学习涵盖了一系列机器学习算法，在过去十年中改变了人工智能的许多重要子领域，并在生物医学的广泛任务中展现出突破性的性能提升。我们预计，未来深度学习将广泛用于预测个性化药物反应，并利用从大型复杂的分子、流行病学、临床和人口统计学数据集中提取的知识优化药物选择和剂量。
