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Additive Conditional Independence for Large and Complex Biological Structures

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Handbook of Statistical Bioinformatics

Part of the book series: Springer Handbooks of Computational Statistics ((SHCS))

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Abstract

In this chapter, we describe a novel statistical relation called additive conditional independence (ACI) and review some of its applications to nonparametric variable selection and graphical models. ACI was recently introduced by Li et al. (2014, JASA) to model three-way relation among random variables. It resembles the core principles of the conditional independence but without resorting to high-dimensional kernel—a distinct feature that provides a balance between parametric and nonparametric models—which also makes itself readily scalable to large datasets. We discuss several ACI-based approaches targeting both variable selectors and network estimations. We also demonstrate the usefulness of ACI through both synthetic and real-world datasets with applications to gene selection and gene regulatory network analyses.

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Acknowledgements

This work is partially supported by the NSF grant CIF-2102243, and the Seed Funding grant from Fox School of Business, Temple University.

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Correspondence to Kuang-Yao Lee .

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Lee, KY., Li, B., Zhao, H. (2022). Additive Conditional Independence for Large and Complex Biological Structures. In: Lu, H.HS., Schölkopf, B., Wells, M.T., Zhao, H. (eds) Handbook of Statistical Bioinformatics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-65902-1_8

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