Horizontal gene transfer (HGT) is a pervasive evolutionary process that results in the distribution of genes between divergent prokaryotic lineages. Phylogenetic distance, shared ecology, and genomic constraints are often cited as key drivers governing horizontal gene transfer, although their relative contributions are unclear. Here, I applied machine learning algorithms to a curated set of diverse bacterial genomes to tease apart the importance of specific functional traits on recent HGT events. We find that functional content accurately predicts the HGT network and performance improves further for transfers involving antibiotic resistance genes (ARGs), highlighting the importance of HGT machinery, niche-specific, and metabolic functions. Our approach is robust at predicting the HGT networks of pathogens, as well as within localized environments.To understand host-microbe interactions that are crucial for normal physiological and immune system development and are implicated in a variety of diseases, we leverage publicly-available interspecies protein-protein interaction data to find clusters of microbiome-derived proteins with high sequence identity to known human-protein interactors. Here I show differential targeting of putative human-interacting bacterial genes in nine independent metagenomic studies, finding evidence that the microbiome broadly targets human proteins involved in immune, oncogenic, apoptotic, and endocrine signaling pathways in relation to inflammatory bowel disease (IBD), colorectal cancer (CRC), obesity, and type 2 diabetes (T2D) diagnoses. Our host-centric analysis provides a mechanistic hypothesis-generating platform and extensively adds human functional annotation to commensal bacterial proteins.