Rapid advances in genomic technologies have led to a wealth of diverse data, from which novel discoveries can be gleaned through the application of robust statistical and computational methods. Here we describe GeneFishing, a computational approach to reconstruct comprehensive context-specific portraits of biological processes by leveraging gene-gene co-expression information. GeneFishing incorporates multiple high-dimensional statistical ideas, including dimensionality reduction, clustering, subsampling and results aggregation, in a novel way to produce robust results. To illustrate the power of our method, we applied it using 21 genes involved in cholesterol metabolic process as “bait”, to “fish out” (or identify) genes not previously implicated in cholesterol metabolism. In particular, application of GeneFishing to the GTEx liver RNAseq data not only re-identified many known cholesterol-related genes, but also discovered glyoxalase 1 (GLO1) as a novel gene implicated in cholesterol metabolism. In a follow-up experiment, we found that GLO1 knock-down in human hepatoma cell lines increased levels of cellular cholesterol ester, validating a role for GLO1 in cholesterol metabolism.