Applications

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Transcriptomic learning for digital pathology

Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk

Boosting Gene Expression Clustering with System-Wide Biological Information: A Robust Autoencoder Approach

Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model

Gene expression inference with deep learning

Learning structure in gene expression data using deep architectures

Exploiting Ladder Networks for Gene Expression Classification

ADAGE – Analysis using Denoising Autoencoders of Gene Expression

Gene Expression Convolutions Using Gene Interaction Graphs

DeepVariant

ProteinGAN: Expanding functional protein sequence space using generative adversarial networks

Realistic in silico generation and augmentation of single cell RNA-seq data using Generative Adversarial Neural Networks

scVAE: Single-cell variational auto-encoders

CellBender remove-background: a deep generative model for unsupervised removal of background noise from scRNA-seq datasets

Creating artificial human genomes using generative models

Privacy-preserving generative deep neural networks support clinical data sharing

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data

MiniFold: a re-implementation of DeepMind's AlphaFold

Structure-Based Function Prediction using Graph Convolutional Networks

Protein Loop Modeling Using Deep Generative Adversarial Network

EVOVAE: Variational autoencoding of Protein Sequences

Deep-RBPPred: Predicting RNA binding proteins in the proteome scale based on deep learning

Deep Learning Model for Predicting Tumor Suppressor Genes and Oncogenes from PDB Structure

Pcons2 – Improved Contact Predictions Using the Recognition of Protein Like Contact Patterns