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

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 s...

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Bibliographic Details
Published in:arXiv.org 2022-09
Main Authors: Kadavath, Saurav, Paradis, Samuel, Yeung, Jacob
Format: Article
Language:English
Subjects:
Artificial neural networks
Combinatorial analysis
Gene expression
Generative adversarial networks
Histones
Parameters
Predictions
Online Access:Get full text
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Summary: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 \footnote{\url{github.com/ssss1029/gene_expression_294}}.\parfillskip=0pt
ISSN:2331-8422