Reconstructing gene regulatory networks from transcriptomic data remains a challenging problem. We adapted the time-lagged Ordered Lasso, a regularized regression method with temporal monotonicity constraints, for network reconstruction from time-series gene expresson data. We also developed a semi-supervised variant that embeds prior network information into the Ordered Lasso to discover novel regulatory dependencies in existing pathways.
