Charles E. McAnany

This paper introduces PISA, a tool for interpreting sequence-to-function neural networks, enabling the discovery of nucleosome-positioning motifs and understanding cis-regulatory code through MNase-seq data.

The paper uses interpretable deep learning to identify sequence rules governing Hippo signaling in mouse trophoblast stem cells, revealing how transcription factors influence enhancer activity.

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.