bioRxivBioinformaticsPreprint
BindSpace: decoding transcription factor binding signals by large-scale joint embedding
Han YuaniD, Meghana KshirsagariD, Lee ZamparoiD, Yuheng LuiD, Christina S. LeslieiD
Abstract
Decoding transcription factor (TF) binding signals in genomic DNA is a fundamental problem. Here we present a prediction model called BindSpace that learns to embed DNA sequences and TF class/family labels into the same space. By training on binding data for hundreds of TFs and embedding over 1M DNA sequences, BindSpace achieves state-of-the-art multiclass binding prediction performance, in vitro and in vivo, and can distinguish signals of closely related TFs.
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant effect prediction
- approach: deep learning
- approach: contrastive learning
- Citations
- 1
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