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Title: Statistical inference of discrete combinatorial functional dependency in biological systems
Inference of a combinatorial function from multiple independent variables (parents) to a dependent variable (child) in a discrete space can be useful in detecting nonlinear relationships in biological systems. Popular conditional independency measures, heavily used in combinatorial inference, are often insensitive to the direction of functional dependency. To address this issue, we define multivariate and conditional functional chi-squared statistics. We also present an algorithm called CFDF for bivariate discrete function inference via an exclusive-effect strategy, in order to identify a best parent set for a given child. It requires each parent to make sufficient contribution beyond any marginal effect. Simulation studies suggest a marked advantage of our framework over alternatives. Applying the method to transcriptome data in genetically perturbed biological systems, we reproduced combinatorial gene interactions known in the literature. Most importantly, we identified combinatorial patterns from joint RNA and protein data to rebut a dispute on the founding principle of molecular biology.
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Proceedings of the 14th Machine Learning in Computational Biology (MLCB) Meeting
Sponsoring Org:
National Science Foundation
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