PAIRWISE NONLINEAR DEPENDENCE ANALYSIS OF GENOMIC DATA. Article Swipe

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Siqi Xiang , Wan Zhang , Siyao Liu , Katherine A. Hoadley , Charles M. Perou , Kai Zhang , J. S. Marron ·

YOU? · · 2025 · Open Access · · DOI: https://doi.org/10.17615/0jnc-x739

In The Cancer Genome Atlas (TCGA) data set, there are many interesting nonlinear dependencies between pairs of genes that reveal important relationships and subtypes of cancer. Such genomic data analysis requires a rapid, powerful and interpretable detection process, especially in a high-dimensional environment. We study the nonlinear patterns among the expression of pairs of genes from TCGA using a powerful tool called Binary Expansion Testing. We find many nonlinear patterns, some of which are driven by known cancer subtypes, some of which are novel.

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Pairwise comparison Nonlinear system Computational biology Biology Mathematics Computer science Statistics Physics Quantum mechanics

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Type: article
Language: en
Landing Page: https://doi.org/10.17615/0jnc-x739
OA Status: green
Related Works: 10
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DOI: https://doi.org/10.17615/0jnc-x739

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Title: PAIRWISE NONLINEAR DEPENDENCE ANALYSIS OF GENOMIC DATA.

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Type: article

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Language: en

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Publication year: 2025

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Publication date: 2025-02-11

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Authors: Siqi Xiang, Wan Zhang, Siyao Liu, Katherine A. Hoadley, Charles M. Perou, Kai Zhang, J. S. Marron

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Landing page: https://doi.org/10.17615/0jnc-x739

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OA URL: https://doi.org/10.17615/0jnc-x739

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Concepts: Pairwise comparison, Nonlinear system, Computational biology, Biology, Mathematics, Computer science, Statistics, Physics, Quantum mechanics

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Related works (count): 10

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