Recent News


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Oct 2021:   Adverse Drug Effects During the Pandemic

The COVID-19 pandemic has reshaped health and medicine in ways both dramatic and subtle. Some of the less obvious shifts can only emerge from analysis of millions of pieces of data—patient records, medical notes, clinical encounter reports. Check out the story in Harvard Medicine News highlighting our research.

Oct 2021:   Graph-Guided Networks for Time Series

New preprint! We introduce Raindrop, a graph-guided network for learning representations of irregularly sampled multivariate time series.

Oct 2021:   Massive Analysis of Differential Adverse Events

Hot off the press in Nature Computational Science! We develop an algorithmic approach for massive analysis of drug adverse events. Our analyses of 10,443,476 adverse event reports have implications for safe medication use and public health policy, and can enable comparison of COVID-19 pandemic to other health emergencies.

Sep 2021:   Leveraging Cell Ontology to Classify Cell Types

Hot off the press in Nature Communications! We developed OnClass, an algorithm and accompanying software for automatically classifying cells into cell types that are part of the controlled vocabulary that forms the Cell Ontology.

Sep 2021:   Major New Release of TDC

We are very excited to announce a major release of Therapeutics Data Commons! In the 0.3.0 release we restructured the codebase, simplified the backend and kept user interfaces the same. We also provide detailed documentation for our TDC package.

Aug 2021:   Trustworthy AI for Healthcare at AAAI

We will be organizing a meeting on Trustworthy AI for Healthcare at AAAI 2022. Stay tuned for details and call for papers.

Aug 2021:   New Paper on Therapeutics Data Commons

Our latest paper on Therapeutics Data Commons: Machine Learning Datasets and Tasks for Drug Discovery and Development will appear at NeurIPS. We are excited to contribute novel datasets and benchmarks in the broad area of therapeutics.

Aug 2021:   AI for Science at NeurIPS

We are organizing the AI for Science workshop at NeurIPS 2021 and have a stellar lineup of invited speakers.

Aug 2021:   Best Poster Award at ICML Comp Biology

Congratulations to Michelle for winning the Best Poster Award for her work on deep contextual learners for protein networks at the ICML Workshop on Computational Biology.

Jul 2021:   Best Paper Award at ICML Interpretable ML

Our short paper on Interactive Visual Explanations for Deep Drug Repurposing received the Best Paper Award at the ICML 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.

Zitnik Lab  ·  Harvard  ·  Department of Biomedical Informatics