22 citations · 46 across the 5 of their papers we have counts for
5 papers
An open unified deep graph learning framework for discovering drug leads
Yueming Yin, Haifeng Hu, Zhen Yang +4
Computational discovery of ideal lead compounds is a critical process for modern drug discovery. It comprises multiple stages: hit screening, molecular property prediction, and mol…
CloudPred: Predicting Patient Phenotypes From Single-cell RNA-seq
Bryan He, Matthew Thomson, Meena Subramaniam +3
Single-cell RNA sequencing (scRNA-seq) has the potential to provide powerful, high-resolution signatures to inform disease prognosis and precision medicine. This paper takes an imp…
ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems
Bharathan Balaji, Jordan Bell-Masterson, Enes Bilgin +7
Reinforcement Learning (RL) has achieved state-of-the-art results in domains such as robotics and games. We build on this previous work by applying RL algorithms to a selection of…
Single-cell eQTLGen Consortium: a personalized understanding of disease
Monique G. P. van der Wijst, Dylan H. de Vries, Hilde E. Groot +11
In recent years, functional genomics approaches combining genetic information with bulk RNA-sequencing data have identified the downstream expression effects of disease-associated…
Genetic Architect: Discovering Genomic Structure with Learned Neural Architectures
Laura Deming, Sasha Targ, Nate Sauder +2
Each human genome is a 3 billion base pair set of encoding instructions. Decoding the genome using deep learning fundamentally differs from most tasks, as we do not know the full s…