# Doubly Non-Central Beta Matrix Factorization for DNA Methylation Data

arXiv: https://arxiv.org/abs/2106.06691 · HTML: https://mlsanity.com/paper/2106.06691/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 12 Jun 2021.

## Builds on (most cited first, 3 of 3)

- [Understanding predictive information criteria for Bayesian models](https://mlsanity.com/paper/1307.5928/) (2013)
- [Poisson-Randomized Gamma Dynamical Systems](https://mlsanity.com/paper/1910.12991/) (2019)
- [Locally Private Bayesian Inference for Count Models](https://mlsanity.com/paper/1803.08471/) (2018)

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

- [Doubly Non-Central Beta Matrix Factorization for Stable Dimensionality Reduction of Bounded Support Matrix Data](https://mlsanity.com/paper/2410.18425/) (2024)

## Abstract (arXiv)

We present a new non-negative matrix factorization model for (0,1) bounded-support data based on the doubly non-central beta (DNCB) distribution, a generalization of the beta distribution. The expressiveness of the DNCB distribution is particularly useful for modeling DNA methylation datasets, which are typically highly dispersed and multi-modal; however, the model structure is sufficiently general that it can be adapted to many other domains where latent representations of (0,1) bounded-support data are of interest. Although the DNCB distribution lacks a closed-form conjugate prior, several augmentations let us derive an efficient posterior inference algorithm composed entirely of analytic updates. Our model improves out-of-sample predictive performance on both real and synthetic DNA methylation datasets over state-of-the-art methods in bioinformatics. In addition, our model yields meaningful latent representations that accord with existing biological knowledge.