Discovery of meaningful associations in genomic data using partial correlation coefficients

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Data Based Identification and Prediction of Nonlinear and Complex Dynamical Systems (2016, methodology): “Partial correlation and information theory (PCIT) [304,305] extract all possible interaction triangles and apply Data Processing Inequality (DPI) to filter indirect interactions.”
Benchmarking algorithms for gene regulatory network inference from single-cell transcriptomic data (2019, methodology): “There is a history of GRN inference algorithms that explicitly try to avoid indirect effects, using techniques such as partial correlation coefficients [37], the data processing inequality [33, 38], and conditioning the score of a pair of genes on all other genes [12].”
Statistical Applications in Genetics and Molecular Biology A Shrinkage Approach to Large-Scale Covariance Matrix Estimation and Implications for Functional Genomics (2011, unknown): “In this context it is also interesting to note that the difficulty of obtaining reliable estimates of˜P has led some researchers to instead consider partial correlations of limited order (e.g., de la Fuente et al., 2004; Wille et al., 2004; Magwene and Kim, 2004).”