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• W3159102436 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." @default.
• W3159102436 created "2021-05-10" @default.
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• W3159102436 date "2021-01-10" @default.
• W3159102436 modified "2023-10-16" @default.
• W3159102436 title "Recursive Convolutional Neural Networks for Epigenomics" @default.
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• W3159102436 doi "https://doi.org/10.1109/icpr48806.2021.9412272" @default.
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