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- W3158033863 abstract "Accurate recognition and annotation of the important functional elements in the genome is an important prerequisite to understand the coding mode of complex regulatory networks in the one-dimensional genome. Despite rapid advances in sequencing and recognition technologies, accurately calling non-coding variant effects from large-scale sequence reads remains challenging. Here we present a deep neural network-based algorithmic framework, DeepMSA, which directly learns a regulatory sequence code from large-scale chromatin-profiling data,enabling to evaluate chromatin effects caused by SNP(single nucleotide polymorphism)." @default.
- W3158033863 created "2021-05-10" @default.
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- W3158033863 date "2021-03-01" @default.
- W3158033863 modified "2023-09-28" @default.
- W3158033863 title "Predicting functional elements and variants effects in non-coding regions based on deep learning" @default.
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- W3158033863 doi "https://doi.org/10.1109/healthcom49281.2021.9398970" @default.
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