BMC Bioinformatics · 2018 · 154 citations · 31 references
We developed a deep-learning-based framework to predict and characterize m6A-containing sequences and hope to help investigators to gain more insights for m6A research. The source code is available at https://github.com/rreybeyb/DeepM6ASeq .
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Sepp Hochreiter, Jürgen Schmidhuber · Neural Computation · 1997 · 93.8K citations
Dropout: a simple way to prevent neural networks from overfitting
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Model-based Analysis of ChIP-Seq (MACS)
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CD-HIT: accelerated for clustering the next-generation sequencing data
LiMin Fu, Beifang Niu, Zhengwei Zhu et al. · Bioinformatics · 2012 · 11.1K citations · Full text
Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq
Dan Dominissini, Sharon Moshitch-Moshkovitz, Schraga Schwartz et al. · Nature · 2012 · 4.9K citations