Recent decade brought a lot of excitement into molecular biology. With the advancements in measurement technology, we can now measure expression of thousands of genes simultaneously. This enabled us to develop biomarker sets predictive of diseases such as asthma. Although they have important prognostic value, biomarker sets are usually not very interpretable. In this work, we developed a novel algorithm, NeTFactor, that uses a computationally-inferred context-specific gene regulatory network and applies topological, statistical, and optimization methods to identify a minimal set of regulators underlying a biomarker set. The paper can be found here.