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Jul 2021:   Best Paper Award at ICML Interpretable ML for Healthcare

Our short paper on Interactive Visual Explanations for Deep Drug Repurposing received the Best Paper Award at ICML 2021 Interpretable ML in Healthcare Workshop. Stay tuned for more news on this evolving project.

Jul 2021:   Five presentations at ICML 2021

Jun 2021:   Theory and Evaluation for Explanations

We introduce the first axiomatic framework for theoretically analyzing, evaluating, and comparing GNN explanation methods. We formalize key properties that all methods should satisfy to generate reliable explanations: faithfulness, stability, and fairness.

Jun 2021:   Deep Contextual Learners for Protein Networks

New preprint on contextualized protein embeddings aims to characterize genes with disease-specific interactions and elucidate disease manifestation in specific cell types.

May 2021:   New Paper Accepted at UAI

Our unified framework for fair and stable graph representation learning has just been accepted at UAI. We establish a theoretical connection between counterfactual fairness and stability and use it in a framework that can be used with any GNN to learn fair and stable embeddings.

Apr 2021:   Hot Off the Press: COVID-19 Repurposing in PNAS

Hot off the press! We deployed AI/ML and network medicine algorithms to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. We screened in human cells the top-ranked drugs, identifying six drugs that reduced viral infection, four of which could be repurposed to treat COVID-19.

Apr 2021:   Representation Learning for Biomedical Nets

In our survey on representation learning for biomedical networks we discuss how long-standing principles of network biology and medicine provide the conceptual grounding for representation learning, explain its successes, and inform future advances.

Mar 2021:   Receiving Amazon Research Award

We are excited about receiving Amazon Faculty Research Award on Actionable Graph Learning for Finding Cures for Emerging Diseases. Thank you to Amazon Science for supporting our research.

Mar 2021:   Michelle's Graduate Research Fellowship

Michelle M. Li won the NSF Graduate Research Fellowship Award. Congratulations!

Mar 2021:   Hot Off the Press: Multiscale Interactome

Hot off the press! We develop a multiscale interactome approach to explain disease treatments. The approach can predict drug-disease treatments, identify proteins and biological functions related to treatment, and identify genes that alter treatment’s efficacy and adverse reactions.

Mar 2021:   Graph Networks in Computational Biology

We are excited to share slides from our recent lecture on Graph Neural Networks in Computational Biology, which we gave at Stanford ML for Graphs course.

Mar 2021:   Fair and Stable Graph Representation Learning

We are thrilled to share the latest preprint on fair and stable graph representation learning.

Feb 2021:   New Preprint on Therapeutics Data Commons

Jan 2021:   An Algorithmic Approach to Patient Safety

The new algorithmic approach investigates population-scale patient safety data and reveals inequalities in adverse events before and during COVID-19 pandemic.

Jan 2021:   Workshop on AI in Health at the Web Conference

We are excited to co-organize Workshop on AI in Health: Transferring and Integrating Knowledge for Better Health at the Web (WWW) conference. The call for papers is open! We also announce the AI in Health Data Challenge.

Jan 2021:   Tutorial on ML for Drug Development

We will present a tutorial on ML/AI for drug discovery and development at IJCAI conference. See the tutorial website.

Dec 2020:   Two New Papers Published

Dec 2020:   Bayer Early Excellence in Science Award

Our research won the Bayer Early Excellence in Science Award. We are honored to have received this recognition!

Nov 2020:   Therapeutics Data Commons (TDC)

We are thrilled to announce Therapeutics Data Commons (TDC)! We invite you to join TDC. TDC is an open-source and community-driven effort.

Nov 2020:   National Symposium on the Future of Drugs

On behalf of the NSF, we are organizing the National Symposium on Drug Repurposing for Future Pandemics. We have a stellar lineup of invited speakers! Register at www.drugsymposium.org.

Oct 2020:   MARS: Novel Cell Types in Single-cell Datasets

Sep 2020:   Four Papers Accepted at NeurIPS

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

Sep 2020:   MITxHarvard Women in AI Interview

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

Aug 2020:   Trustworthy AI for Healthcare

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!

Aug 2020:   Network Drugs for COVID-19

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!

Jul 2020:   Podcast on ML for Drug Development

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.

Jul 2020:   Postdoctoral Research Fellowship

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.

Jul 2020:   DeepPurpose Library

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

Jun 2020:   Subgraph Neural Networks

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

Jun 2020:   Defense Against Adversarial Attacks

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.

Jun 2020:   Graph Meta Learning via Subgraphs

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.

May 2020:   The Open Graph Benchmark

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

May 2020:   Special Issue on AI for COVID-19

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.

May 2020:   Multiscale Interactome

May 2020:   Molecular Interaction Networks

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.

Apr 2020:   Submit to PhD Forum at ECML

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.

Apr 2020:   Drug Repurposing for COVID-19

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

Mar 2020:   AI Cures

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.

Mar 2020:   COVID-19 Task Force

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

Mar 2020:   Graph ML Workshop at ICML 2020

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

Mar 2020:   Accepted Tutorial at IJCAI!

We will present a tutorial on Machine Learning for Drug Development at IJCAI 2020! Stay tuned for details.

Mar 2020:   Welcome New Students!

Haoxin, Michelle, and Xiang joined the lab. Welcome! We look forward to seeing you all in the lab!

Feb 2020:   Meta Learning for Single-cell Biology

A new preprint on meta learning for identifying and naming cell types, even cell types that have never been seen before and do not exist in the training data. Check it out!

Dec 2019:   Pre-training Graph Neural Networks

Dec 2019:   Deep Learning for Network Biology

Marinka is co-editing a special issue of ACM/IEEE TCBB on Deep learning and graph embeddings for network biology. Submit your finest work!

Dec 2019:   Welcome New Students!

Kathleen, Jingyi, Kexin, Yujie, and Stone joined the lab. Welcome!

Zitnik Lab  ·  Harvard  ·  Department of Biomedical Informatics