Quantitative proteomics offers transformative insights into molecular mechanisms underlying complex diseases; however, the inherent noise and variability in proteomic data pose significant challenges for accurate signal detection. This led to the development of the Maximal Aggregation of Good protein signal from Mass spectrometric data (MAGMa) tool, which leverages rigorous statistical testing to achieve a balance between sensitivity and specificity, effectively filtering out noise and enabling robust identification of subtle, true biological signals. MAGMa’s utility was validated on benchmarking datasets, where it consistently outperformed existing methods in identifying accurate interaction signals. MAGMa was applied to disease models with high therapeutic relevance, including SARS-CoV-2 and onco-fusion-driven cancers. In SARS-CoV-2 studies, MAGMa revealed a detailed host-pathogen interactome by identifying novel viral-host protein interactions. This approach led to the discovery of potential antiviral targets, with the drug carvedilol demonstrating promising antiviral effects in follow-up studies. In cancer models, MAGMa enabled us to dissect the altered protein interaction networks driven by oncogenic fusion proteins, identifying critical perturbations in the SWI/SNF chromatin remodeling complex in sarcoma. These findings shed light on the molecular underpinnings of fusion-mediated oncogenesis and open avenues for targeted cancer therapies. This corpus of work explores how network dynamics inform disease progression and the potential for computational tools to refine protein biomarkers as close phenotypic readouts. Results presented herein demonstrate how rigorous statistical methods can enhance the precision of proteomic analyses, making it possible to unravel complex biological interactions and prioritize drug targets. MAGMa provides researchers with a powerful tool to deepen insights into disease mechanisms and accelerate therapeutic discovery across a range of pathologies.