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An image representation based convolutional network for DNA classification

Abstract : The folding structure of the DNA molecule combined with helper molecules, also referred to as the chromatin, is highly relevant for the functional properties of DNA. The chromatin structure is largely determined by the underlying primary DNA sequence, though the interaction is not yet fully understood. In this paper we develop a convolutional neural network that takes an image-representation of primary DNA sequence as its input, and predicts key determinants of chromatin structure. The method is developed such that it is capable of detecting interactions between distal elements in the DNA sequence, which are known to be highly relevant. Our experiments show that the method outperforms several existing methods both in terms of prediction accuracy and training time.
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Conference papers
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Contributor : Marie-France Sagot Connect in order to contact the contributor
Submitted on : Sunday, December 23, 2018 - 1:57:50 PM
Last modification on : Tuesday, August 13, 2019 - 10:16:03 AM

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  • HAL Id : hal-01964736, version 1
  • ARXIV : 1806.04931



Bojian Yin, Marleen Balvert, Davide Zambrano, Alexander Schönhuth, Sander Bohte. An image representation based convolutional network for DNA classification. ICLR 2018 - 6th International Conference on Learning Representations, Apr 2018, Vancouver, Canada. pp.1-17. ⟨hal-01964736⟩



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