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• W3014879134 abstract "Abstract Deep learning methods have proved to be powerful classification tools in the fields of structural and functional genomics. In this paper, we introduce a 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." @default.
• W3014879134 created "2020-04-10" @default.
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• W3014879134 date "2020-04-03" @default.
• W3014879134 modified "2023-10-16" @default.
• W3014879134 title "Recursive Convolutional Neural Networks for Epigenomics" @default.
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• W3014879134 doi "https://doi.org/10.1101/2020.04.02.021519" @default.
• W3014879134 hasPublicationYear "2020" @default.
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