This study explores the genetic regulators of microRNAs (miRNAs) using a set of machine learning models to predict miRNA expression levels from gene expression data. Employing machine learning, we accurately predicted the expression of 353 human miRNAs (R² > 0.5), revealing robust miRNA–gene regulatory relationships. By analyzing the coefficients of these predictive models, we identified genetic regulators for each miRNA and highlighted the multifactorial nature of miRNA regulation. Further network analysis uncovered that miRNAs with higher predictive accuracy are more densely connected to their top predictive genes, reflecting strong regulatory control within miRNA–gene networks. To refine these insights, we filtered the miRNA–gene interaction networks to identify miRNAs specifically associated with enriched pathways, such as synaptic function and cardiovascular processes. From this pathway-centric analysis, we present a curated list of miRNAs and their genetic regulators, pinpointing their activity within distinct biological contexts. Additionally, our study provides a comprehensive set of metrics and coefficients for the genes most predictive of miRNA expression, along with a filtered subnetwork of miRNAs linked to specific pathways and phenotypes. By integrating miRNA expression predictors with network analysis and pathway enrichment, this work advances our understanding of miRNA regulatory mechanisms and their roles across distinct biological systems. Our approach enables researchers to train custom models using TCGA data and predict miRNA expression from gene expression inputs.

Mass spectrometry (MS)-based approaches have significantly advanced our ability to study protein interaction networks in an unbiased manner. Here, we present a protocol that uses affinity purification (AP)-MS to identify interaction partners of a biotinylated transcription factor of interest, isolated from primary murine T cells. The resulting interactome data are integrated with motif analyses from chromatin immunoprecipitation sequencing (ChIP-seq) experiments. This combined approach facilitates the concurrent identification of protein interactors and composite DNA motifs, with each dataset corroborating the findings of the other. For complete details on the use and execution of this protocol, please refer to Gabele et al.¹.