Fully convolutional deep learning variant effect predictor architecture

Data-driven design of context-specific regulatory elements

Sequence-based Modeling of single-cell ATAC-seq using Convolutional Neural Networks.

Deep learning model built to quantitatively predict the activities of developmental and housekeeping enhancers from DNA sequence in Drosophila melanogaster S2 cells

Open source code for AlphaFold 2.

A neural network framework for predicting the Hi-C chromatin interactions from megabase scale DNA sequence

Sequential regulatory activity predictions with deep convolutional neural networks.

a framework for training sequence-level deep learning networks

Code for running RFdiffusion

code to run sei and obtain sei and sequence class predictions

accurate prediction of promoter activity and variant effects from massive parallel reporter assays

Official repository for the paper "Large-scale clinical interpretation of genetic variants using evolutionary data and deep learning". Joint collaboration between the Marks lab and the OATML group.

Protein design and variant prediction using autoregressive generative models