Abstract
Systems biology aims to unravel the vast network of functional interactions that govern biological systems. To date, the inference of gene interactions from large-scale 'omics data is typically achieved using correlations. We present the hierarchical interaction score (HIS) and show that the HIS outperforms commonly used methods in the inference of functional interactions between genes measured in large-scale experiments, making it a valuable statistic for systems biology.
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