Altucci Lucia, Badimon Lina, Balligand Jean-Luc, Baumbach Jan, Catapano Alberico L, Cheng Feixong, DeMeo Dawn, Gupta Rajat, Hacker Marcus, Liu Yang-Yu, Loscalzo Joseph, Maniscalco Sabrina, Menche Jörg, Menichetti Giulia, Parini Paolo, Schmidt Harald H H W, Zitnik Marinka
Department of Precision Medicine, University of Campania Luigi Vanvitelli, Naples, Italy.
Cardiovascular Program, Catalan Institute of Cardio-Vascular Science (ICCC) and Spanish Research Consortium on Cardiovascular Diseases, Research Institute Hospital de la Santa Creu i Sant Pau, Barcelona.
NEJM AI. 2025 Sep;2(9). doi: 10.1056/aira2401229. Epub 2025 Aug 28.
Over the past two decades, network medicine (NM) has evolved to help define disease mechanisms, identify drug targets, and guide increasingly precise therapies. In recent years, the integration of NM with artificial intelligence (AI), particularly deep learning techniques, has evolved with increasing applications. AI techniques help elucidate complex disease mechanisms and define precise therapies. The depth of useful, mechanistic information implicit in molecular interaction networks and prior deep learning successes provide a rational basis for combining NM and AI in the analyses of large multiomic datasets to enhance the speed, predictive precision, and biological insights of the computational process. In this review, we provide a summary of concepts related to the combined use of AI and NM as a path to precision medicine, illustrating the success of this joint approach to biomedical complexity and its ongoing challenges.
在过去二十年中,网络医学(NM)不断发展,以帮助确定疾病机制、识别药物靶点并指导日益精准的治疗。近年来,NM与人工智能(AI),尤其是深度学习技术的整合随着应用的增加而不断发展。AI技术有助于阐明复杂的疾病机制并确定精准的治疗方法。分子相互作用网络中隐含的有用的机制信息的深度以及先前深度学习的成功为在大型多组学数据集分析中结合NM和AI提供了合理依据,以提高计算过程的速度、预测精度和生物学见解。在本综述中,我们总结了与联合使用AI和NM作为精准医学途径相关的概念,阐述了这种联合方法在应对生物医学复杂性方面的成功及其面临的持续挑战。