Abstract
Here we describe an approach that uses deep learning neural networks such as CNN and RNN to aggregate information from DNA sequence; physical, chemical, and structural properties of nucleotides; and omics data on histone modifications, methylation, chromatin accessibility, and transcription factor binding sites and data from other available NGS experiments. We explain how with the trained model one can perform whole-genome annotation of Z-DNA regions and feature importance analysis in order to define key determinants for functional Z-DNA regions.
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Beknazarov, N., Poptsova, M. (2023). DeepZ: A Deep Learning Approach for Z-DNA Prediction. In: Kim, K.K., Subramani, V.K. (eds) Z-DNA. Methods in Molecular Biology, vol 2651. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3084-6_15
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DOI: https://doi.org/10.1007/978-1-0716-3084-6_15
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