标题： 人工智能在推进精准医学中的机会 显示英文标题

标题： Opportunities for artificial intelligence in advancing precision medicine

摘要： 机器学习（ML）、深度学习（DL）和人工智能（AI）在生物医学中的重要性日益增加。 本工作的目标是展示数字健康中机器学习的进展，举例说明未来的需要和趋势，并确定AI和ML在精准健康中的关键前提条件。 高通量技术正在提供越来越多的生物医学数据，例如大规模全基因组测序检测、医学图像库或健康、发育和患病组织的药物扰动筛选。 生物医学中的多组学数据是深度且复杂的，为数据驱动的洞察和自动疾病分类提供了机会。 从这些数据中学习将扩展我们对健康基线和疾病特征的理解和定义。 深度神经网络的最新应用包括数字图像识别、单细胞聚类和虚拟药物筛选，展示了机器学习在生物医学中的广度和力量。 显著的是， AI和系统生物学已经接受了大数据挑战，并可能促进新型生物技术衍生疗法，以推动精准医疗方法的实施。 显示英文摘要

摘要： Machine learning (ML), deep learning (DL), and artificial intelligence (AI) are of increasing importance in biomedicine. The goal of this work is to show progress in ML in digital health, to exemplify future needs and trends, and to identify any essential prerequisites of AI and ML for precision health. High-throughput technologies are delivering growing volumes of biomedical data, such as large-scale genome-wide sequencing assays, libraries of medical images, or drug perturbation screens of healthy, developing, and diseased tissue. Multi-omics data in biomedicine is deep and complex, offering an opportunity for data-driven insights and automated disease classification. Learning from these data will open our understanding and definition of healthy baselines and disease signatures. State-of-the-art applications of deep neural networks include digital image recognition, single cell clustering, and virtual drug screens, demonstrating breadths and power of ML in biomedicine. Significantly, AI and systems biology have embraced big data challenges and may enable novel biotechnology-derived therapies to facilitate the implementation of precision medicine approaches.