Inferring gene interaction networks from transcriptomic data is a central challenge in systems biology. Most existing approaches emphasize population level relationships, thereby obscuring biologically meaningful inter‑individual heterogeneity. While single‑sample network inference methods aim to address this limitation, many rely on the contrasts between reference and perturbed aggregate networks, potentially reducing the signals from the fundamental gene-gene relationships shared across samples. Here, we introduce ssNITE (Single‑Sample Network Inference Through Trees in Ensemble), a novel framework inspired by GENIE3, a top-performing population level gene regulatory network inference method in the DREAM challenges, for constructing sample‑specific gene interaction networks. ssNITE leverages Random Forest regression models trained on bulk transcriptomic data to extract individualized gene dependencies by tracing sample‑specific decision paths in the trees of the forest. Applied to breast cancer transcriptomic data from The Cancer Genome Atlas, ssNITE produces networks that better aligns with known clinical information than its peers. Particularly, features derived from ssNITE networks exhibited stronger associations with clinical phenotypes, improved predictive performance in molecular subtype classification and tumour purity estimation, and enhanced prognostic value for overall survival. By directly coupling predictive modelling with individualized network reconstruction, ssNITE provides a scalable and interpretable approach for sample‑specific network inference, advancing methodological foundations for individualised network medicine.

Co-authors: Teemu Rintala, Vittorio Fortino