Molecular mechanisms of complex diseases such as cancer often vary across patients, leading to differences in risk, progression, and treatment response. The central premise of precision medicine is to tailor preventative and therapeutic strategies to individual patients, by accounting for this heterogeneity in disease mechanisms. Recently, gene regulatory networks, which capture interactions among genes and their molecular regulators, have proven to be valuable tools for uncovering disease mechanisms, paving the way for network medicine. However, conventional approaches for biological network inference typically estimate population-level networks that average out individual heterogeneity. This limitation arises because individual-level data are scarce, and most statistical methods require reasonably sized samples to extract meaningful signals. In this talk, I will introduce an Empirical Bayes framework for estimating individual-specific networks that reflect person-specific disease biology. By integrating population-level prior information with individual-specific omics data, our framework recovers both shared and unique regulatory patterns across individuals. Our methods are highly scalable to large scale multi-omics datasets. I will describe two such methods: one for individual-specific co- expression networks, and another for individual-specific multi-omic Gaussian graphical models. Applications to simulated and human cancer datasets demonstrate that these methods not only recover accurate interactions between omics data types, but they also reveal patient-level network differences linked to clinical outcomes. This work highlights the potential for Empirical Bayes as a principled and practical strategy for individualized network inference, with broad implications for precision medicine.