Amr M. Alexandari

The paper introduces Affinity Distillation (AD), a method to extract thermodynamic affinities from deep learning models of in vivo TF binding, showing it outperforms motif-based methods in predicting sequence variation impacts.

The paper introduces BPNet, a deep learning model that predicts base-resolution ChIP-nexus binding profiles of TFs and identifies soft syntax rules for cooperative TF binding.

Deep learning rivals or beats baselines across imaging, health records, genomics and drug discovery but is not yet transformative; scarce labels, privacy and opacity remain hurdles.

This paper introduces a separable fully connected layer for deep learning in genomics, showing improved performance and biological consistency over conventional models.