SimpleChrome: Encoding of Combinatorial Effects for Predicting Gene Expression

• URL: http://arxiv.org/abs/2012.08671v2
• Date: Thu, 17 Dec 2020 05:58:21 GMT
• Title: SimpleChrome: Encoding of Combinatorial Effects for Predicting Gene Expression
• Authors: Wei Cheng, Ghulam Murtaza, Aaron Wang
• Abstract summary: We present SimpleChrome, a deep learning model that learns the histone modification representations of genes. The features learned from the model allow us to better understand the latent effects of cross-gene interactions and direct gene regulation on the target gene expression.
• Score: 8.326669256957352
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Due to recent breakthroughs in state-of-the-art DNA sequencing technology, genomics data sets have become ubiquitous. The emergence of large-scale data sets provides great opportunities for better understanding of genomics, especially gene regulation. Although each cell in the human body contains the same set of DNA information, gene expression controls the functions of these cells by either turning genes on or off, known as gene expression levels. There are two important factors that control the expression level of each gene: (1) Gene regulation such as histone modifications can directly regulate gene expression. (2) Neighboring genes that are functionally related to or interact with each other that can also affect gene expression level. Previous efforts have tried to address the former using Attention-based model. However, addressing the second problem requires the incorporation of all potentially related gene information into the model. Though modern machine learning and deep learning models have been able to capture gene expression signals when applied to moderately sized data, they have struggled to recover the underlying signals of the data due to the nature of the data's higher dimensionality. To remedy this issue, we present SimpleChrome, a deep learning model that learns the latent histone modification representations of genes. The features learned from the model allow us to better understand the combinatorial effects of cross-gene interactions and direct gene regulation on the target gene expression. The results of this paper show outstanding improvements on the predictive capabilities of downstream models and greatly relaxes the need for a large data set to learn a robust, generalized neural network. These results have immediate downstream effects in epigenomics research and drug development.

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