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We propose an attention-based approach for accurately predicting transcription factor binding sites. Our method combines DNA sequence with partially observed labels from epigenetic experiments to impute the values of missing labels, allowing for better predictions as more label information is known beforehand. We train and evaluate this model on cell lines from the ENCODE consortium and show that our model performs well on standard prediction tasks and further improves when partial data becomes available. The main contributions of our approach are generalization to unseen cell types and informed experimental design. Our model is able to reliably predict binding sites for cell types never seen during training. In addition, we use a beam search to identify the set of experimental labels that maximize prediction accuracy on missing data. The results of this beam search can be used to inform cost-efficient experimental design under limited resources.

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