# Pairwise Nonlinear Dependence Analysis of Genomic Data

arXiv: https://arxiv.org/abs/2202.09880 · HTML: https://mlsanity.com/paper/2202.09880/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2022.

## Built on by (most cited first, 1 of 1)

- [BELIEF in Dependence: Leveraging Atomic Linearity in Data Bits for Rethinking Generalized Linear Models](https://mlsanity.com/paper/2210.10852/) (2022)

## Abstract (arXiv)

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.