Naozumi Hiranuma

The paper introduces DeepAccNet, a deep learning framework that improves protein structure refinement by estimating accuracy and guiding Rosetta refinement.

AIControl improves ChIP-seq peak identification by replacing matched control experiments with machine learning, enhancing accuracy without requiring control datasets.

DeepProfile trains a variational autoencoder on thousands of unlabeled gene expression samples to learn low-dimensional representations that improve prediction of acute myeloid leukemia drug responses and cancer phenotypes.

DeepATAC predicts regulatory factor binding activity from ATAC-seq signals and DNA sequence, outperforming existing methods.