Recursive Convolutional Neural Networks for Epigenomics

Symeonidi A, Nicolaou A, Johannes F, Christlein V (2021)

Publication Type: Conference contribution

Publication year: 2021

Event location: Milan IT

ISBN: 9781728188089

DOI: 10.1109/ICPR48806.2021.9412272

Abstract

Deep learning methods have proved to be powerful classification tools in the fields of structural and functional genomics. In this paper, we introduce Recursive Convolutional Neural Networks (RCNN) for the analysis of epigenomic data. We focus on the task of predicting gene expression from the intensity of histone modifications. The proposed RCNN architecture can be applied to data of an arbitrary size, and has a single meta-parameter that quantifies the models capacity, thus making it flexible for experimenting. The proposed architecture outperforms state-of-the-art systems, while having several orders of magnitude fewer parameters.

Authors with CRIS profile

Vincent Christlein Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Technische Universität München (TUM) DE Germany (DE)

How to cite

APA:

Symeonidi, A., Nicolaou, A., Johannes, F., & Christlein, V. (2021). Recursive Convolutional Neural Networks for Epigenomics. In Proceedings of the 25th International Conference on Pattern Recognition (ICPR). Milan, IT.

MLA:

Symeonidi, Aikaterini, et al. "Recursive Convolutional Neural Networks for Epigenomics." Proceedings of the 25th International Conference on Pattern Recognition (ICPR), Milan 2021.

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