Hot off the press! MARS, an approach for discovering novel cell types across heterogeneous single-cell experiments is just published in Nature Methods.

Thrilled that our lab has 4 papers accepted at NeurIPS 2020! Congratulations to fantastic students and collaborators, Michelle, Xiang, Kexin, Sam, and Emily.

The MITxHarvard Women in AI initiative talked with Marinka about AI, machine learning, and the role of new technologies in biomedical research.

We are excited to be co-organizing a workshop at AAAI 2021 on Trustworthy AI for Healthcare! We have a stellar lineup of speakers. Details to follow soon!

What are network drugs? Drugs for COVID-19 predicted by network medicine, our graph neural networks (GNNs), and our rank aggregation algorithms, followed by experimental confirmations. The full paper is finally out!

Tune in to the podcast with Marinka about machine learning to drug development. The discussion focuses on open research questions in the field, including how to limit the search space of high-throughput screens, design drugs entirely from scratch, and identify likely side-effects of combining drugs in novel ways.

We have a new opening for a postdoctoral research fellow in novel machine learning methods to combat COVID-19! Submit your application by September 1, 2020.

DeepPurpose is a deep learning library for drug-target interaction prediction and applications to drug repurposing and virtual screening.

Subgraph neural networks learn powerful subgraph representations that create fundamentally new opportunities for predictions beyond nodes, edges, and entire graphs.

GNNGuard can defend graph neural networks against a variety of training-time attacks. Remarkably, GNNGuard can restore state-of-the-art performance of any GNN in the face of adversarial attacks.

G-Meta is a meta-learning approach for graphs that quickly adapts to new prediction tasks using only a handful of data points. G-Meta works in most challenging, few-shot learning settings and scales to massive interactomics data as we show on our new Networks of Life dataset comprising of 1,840 networks.

A new paper introducing the Open Graph Benchmark, a diverse set of challenging and realistic benchmark datasets for graph machine learning.

Marinka is co-editing a special issue of IEEE Big Data on AI for COVID-19. In light of the urgent need for data-driven solutions to mitigate the COVID-19 pandemic, the special issue will aim for a fast-track peer review.

A new preprint on discovering disease treatment mechanisms through the multiscale interactome.

A new preprint describing a graph neural network approach for the prediction of molecular interactions, including drug-drug, drug-target, protein-protein, and gene-disease interactions.

The call for ECML-PKDD 2020 PhD Forum Track is now online. If you are a PhD student, submit your work on machine learning and knowledge discovery.

We are excited to share our latest results on how networks and graph machine-learning help us search for a cure for COVID-19.

We are joining AI Cures initiative at MIT! We will develop machine learning methods for finding promising antiviral molecules for COVID-19 and other emerging pathogens.

We are excited to be working with László Barabási and his amazing team of scientists as we search for a cure for COVID-19.

We will co-organize a workshop on Graph Representation Learning and Beyond at ICML 2020. Submit your finest work!