# DeepChrome 2.0: Investigating and Improving Architectures, Visualizations, & Experiments

arXiv: https://arxiv.org/abs/2209.11923 · HTML: https://mlsanity.com/paper/2209.11923/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 13 references that are in the map.
- First posted on arXiv 24 Sep 2022.

## Builds on (most cited first, 2 of 2)

- [Attend and Predict: Understanding Gene Regulation by Selective Attention on Chromatin](https://mlsanity.com/paper/1708.00339/) (2017)
- [DeepDiff: Deep-learning for predicting Differential gene expression from histone modifications](https://mlsanity.com/paper/1807.03878/) (2018)

## Abstract (arXiv)

Histone modifications play a critical role in gene regulation. Consequently, predicting gene expression from histone modification signals is a highly motivated problem in epigenetics. We build upon the work of DeepChrome by Singh et al. (2016), who trained classifiers that map histone modification signals to gene expression. We present a novel visualization technique for providing insight into combinatorial relationships among histone modifications for gene regulation that uses a generative adversarial network to generate histone modification signals. We also explore and compare various architectural changes, with results suggesting that the 645k-parameter convolutional neural network from DeepChrome has the same predictive power as a 12-parameter linear network. Results from cross-cell prediction experiments, where the model is trained and tested on datasets of varying sizes, cell-types, and correlations, suggest the relationship between histone modification signals and gene expression is independent of cell type. We release our PyTorch re-implementation of DeepChrome on GitHub footnoteurlgithub.com/ssss1029/geneexpression294.parfillskip=0pt