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The causal inference framework for gene regulation, based on the theory of probabilistic graphical models, provides a systematic approach to identify cause-and-effect relationships between genes. This framework integrates genomic data with prior knowledge and experimental validation, allowing researchers to predict the directionality of gene interactions from large-scale omics data sets.

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The causal inference framework for gene regulation, based on the theory of probabilistic graphical models, provides a systematic approach to identify cause-and-effect relationships between genes. This framework integrates genomic data with prior knowledge and experimental validation, allowing researchers to predict the directionality of gene interactions from large-scale omics data sets.

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November 7, 2024