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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Acknowledgements
The authors would like to acknowledge the Roadmap Epigenomics Mapping Consortium (REMC) for sharing the database. We would also like to thank the powerful bedtools for genome arithmetic.
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Zhu, L., Kesseli, J., Nykter, M., Huttunen, H. (2018). Predicting Gene Expression Levels from Histone Modification Signals with Convolutional Recurrent Neural Networks. In: Eskola, H., Väisänen, O., Viik, J., Hyttinen, J. (eds) EMBEC & NBC 2017. EMBEC NBC 2017 2017. IFMBE Proceedings, vol 65. Springer, Singapore. https://doi.org/10.1007/978-981-10-5122-7_139
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DOI: https://doi.org/10.1007/978-981-10-5122-7_139
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