Identifying complex sequence patterns in massive omics data with a variable-convolutional layer in deep neural network
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Summary
A novel convolutional layer for deep neural network, named Variable Convolutional (vConv) layer, is proposed, for effective motif identification in high-throughput omics data by learning kernel length from data adaptively.
- Published
- 2021-01-01
- Cited by
- 0
- References
- 49
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:231896378
References
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- Motif discovery and transcription factor binding sites before and after the next-generation sequencing era
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- Perspectives on the RNA Polymerase II Core Promoter
- SIOMICS: a novel approach for systematic identification of motifs in ChIP-seq data
- MEME: discovering and analyzing DNA and protein sequence motifs
- Binding site discovery from nucleic acid sequences by discriminative learning of hidden Markov models
- MEME-ChIP: motif analysis of large DNA datasets
- Deep and wide digging for binding motifs in ChIP-Seq data
- A survey of DNA motif finding algorithms
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