Predicting Gene Expression Levels from Histone Modification Signals with Convolutional Recurrent Neural Networks

Lingyu Zhu,Heikki Huttunen,Matti Nykter,Juha Kesseli

doi:10.1007/978-981-10-5122-7_139

Predicting Gene Expression Levels from Histone Modification Signals with Convolutional Recurrent Neural Networks

Lingyu Zhu, Heikki Huttunen + Show 2 more

https://doi.org/10.1007/978-981-10-5122-7_139

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Publication Date: Jun 13, 2017

Citations: 1

Affiliation: Tampere University

#Convolutional Recurrent Neural Network #Convolutional Neural Network + Show 8 more

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Abstract

In this paper we study how a Convolutional Recurrent Neural Network performs for predicting the gene expression levels from histone modification signals. Moreover, we consider two simplified variants of the Convolutional Recurrent Neural Network: Convolutional Neural Network and Recurrent Neural Network. The performance of the methods is evaluated with histone modification signal and gene expression data derived from Roadmap Epigenomics Mapping Consortium database, and compared against the state of the art method: the DeepChrome. It is shown that the proposed models give a statistically significant improvement over the baseline.

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