Max R. Highsmith

Generative genomics accurately predicts future experimental results

This paper introduces GEM-1, a generative model that accurately predicts future gene expression experiments, achieving performance comparable to real lab experiments.

Prediction of the 4D Chromosome Structure From Time-Series Hi-C Data

This paper introduces 4DMax, a method that predicts dynamic chromosome structures from time-series Hi-C data, effectively interpolating chromatin positions and recovering structural features at unobserved time points.

TAPIOCA: Topological Attention and Predictive Inference of Chromatin Arrangement Using Epigenetic Features

TAPIOCA predicts chromatin topology using epigenetic features without Hi-C data, outperforming existing methods across cell lines.

VEHiCLE: a Variationally Encoded Hi-C Loss Enhancement algorithm

VEHiCLE enhances Hi-C contact map resolution using a variational autoencoder and adversarial training, improving downstream analysis capabilities.

DeepCryoPicker: Fully Automated Deep Neural Network for Single Protein Particle Picking in cryo-EM

DeepCryoPicker automates single particle picking in cryo-EM using deep learning, outperforming semi-automated methods without human intervention.

GSDB: a database of 3D chromosome and genome structures reconstructed from Hi-C data

GSDB provides a comprehensive repository of 3D genome structures reconstructed from Hi-C data, containing over 50,000 structures from 12 methods.

Deep Learning For Denoising Hi-C Chromosomal Contact Data

This paper develops deep learning methods to denoise Hi-C contact matrices and improve chromosome structure predictions, showing consistent improvement in multiple metrics on synthetic yeast data.