This study presents an in silico method to predict protein-protein interactions (PPIs) in human macrophages, integrating PPI data with metadata to infer a context-specific interactome. The approach combines statistical and functional criteria to select a Confidence Subset (CS) of interactions likely to occur in macrophages. The CS was validated using clustering and enrichment analyses, showing it is enriched in interactions related to immune response, apoptosis, and other macrophage-related biological processes. The resulting Contextualized Interactome (CI) includes 30,182 interactions involving 8,633 proteins, representing 75% of the initial dataset. The CI was validated by comparing its functional enrichment with the original dataset and other sources, showing significant enrichment in pathways related to immune response and apoptosis. The study also highlights the importance of considering PPIs rather than gene expression alone for understanding host responses to infections, as PPI analysis reveals key biological processes such as apoptosis that are not evident from gene expression data. The results demonstrate that contextualizing interactomes improves the biological significance of bioinformatic analyses, providing new insights into the mechanisms of pathogen persistence in host cells. The method offers a framework for studying host-pathogen interactions at the functional level, emphasizing the importance of protein interaction networks in understanding cellular processes during infections.This study presents an in silico method to predict protein-protein interactions (PPIs) in human macrophages, integrating PPI data with metadata to infer a context-specific interactome. The approach combines statistical and functional criteria to select a Confidence Subset (CS) of interactions likely to occur in macrophages. The CS was validated using clustering and enrichment analyses, showing it is enriched in interactions related to immune response, apoptosis, and other macrophage-related biological processes. The resulting Contextualized Interactome (CI) includes 30,182 interactions involving 8,633 proteins, representing 75% of the initial dataset. The CI was validated by comparing its functional enrichment with the original dataset and other sources, showing significant enrichment in pathways related to immune response and apoptosis. The study also highlights the importance of considering PPIs rather than gene expression alone for understanding host responses to infections, as PPI analysis reveals key biological processes such as apoptosis that are not evident from gene expression data. The results demonstrate that contextualizing interactomes improves the biological significance of bioinformatic analyses, providing new insights into the mechanisms of pathogen persistence in host cells. The method offers a framework for studying host-pathogen interactions at the functional level, emphasizing the importance of protein interaction networks in understanding cellular processes during infections.