Abstract
Learning causality from biological data remains a challenge. We present MRPC, a novel machine learning algorithm that employs generalized Mendelian randomization and learns a causal biological network with directed edges. Our method has several desirable statistical features: it controls the false discovery rate, and performs robust inference. Using MRPC, we distinguished direct and indirect targets among multiple genes associated with eQTLs, and constructed a network for frequently altered cancer genes.
Copyright
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