Wisconsin Institute for Discovery, 330 N. Orchard Street, Madison, WI 53715, USA; The Max Harry Weil Institute of Critical Care Research & Innovation, University of Michigan, Ann Arbor, MI, USA; Department of Emergency Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.
Wisconsin Institute for Discovery, 330 N. Orchard Street, Madison, WI 53715, USA; Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53715, USA.
Cell Rep Methods. 2023 Sep 25;3(9):100594. doi: 10.1016/j.crmeth.2023.100594.
Computational methods that can predict hard-to-measure modalities from those that are easier to measure, in a patient-specific manner, play a critical role in personalized medicine. In this issue of Cell Reports Methods, Khurana et al. present differential gene targets of accessible chromatin (DGTAC), an approach which predicts patient-specific enhancer-promoter interactions.
能够以患者特异性方式从较易测量的模式预测难以测量的模式的计算方法在个性化医疗中起着关键作用。在本期《Cell Reports Methods》中，Khurana 等人提出了可及染色质的差异基因靶点（DGTAC）方法，该方法可预测患者特异性增强子-启动子相互作用。
