ArticleGenome biology2022
BindVAE: Dirichlet variational autoencoders for de novo motif discovery from accessible chromatin.
Article in Genome biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- STRIVE: interpretable multimodal subtype discovery in Women's cancers using an attention-weighted joint variational autoencoder.Frontiers in bioinformatics · 2026Article
- Article
- Deep topic modeling of spatial transcriptomics in the rheumatoid arthritis synovium identifies distinct classes of ectopic lymphoid structures.bioRxiv : the preprint server for biology · 2025Article
- A single-cell multimodal view on gene regulatory network inference from transcriptomics and chromatin accessibility data.Briefings in bioinformatics · 2024Review
- Machine learning in biological physics: From biomolecular prediction to design.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Uncovering uncharacterized binding of transcription factors from ATAC-seq footprinting data.Scientific reports · 2024Article
- JASPAR 2024: 20th anniversary of the open-access database of transcription factor binding profiles.Nucleic acids research · 2024Article
- Predicting the impact of sequence motifs on gene regulation using single-cell data.Genome biology · 2023Article
- BindVAE: Dirichlet variational autoencoders for de novo motif discovery from accessible chromatin.Genome biology · 2022Article
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Authors and funding
4 authors.
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Abstract
We present a novel unsupervised deep learning approach called BindVAE, based on Dirichlet variational autoencoders, for jointly decoding multiple TF binding signals from open chromatin regions. BindVAE can disentangle an input DNA sequence into distinct latent factors that encode cell-type specific in vivo binding signals for individual TFs, composite patterns for TFs involved in cooperative binding, and genomic context surrounding the binding sites. On the task of retrieving the motifs of expressed TFs in a given cell type, BindVAE is competitive with existing motif discovery approaches.
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Registered trials
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