Recent News

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May 2022:   George Named the 2022 Wojcicki Troper Fellow

May 2022:   New preprint on PrimeKG

New preprint on building knowledge graphs to enable precision medicine applications.

Apr 2022:   Webster on the Cover of Cell Systems

Webster is on the cover of April issue of Cell Systems. Webster uses cell viability changes following gene perturbation to automatically learn cellular functions and pathways from data.

Apr 2022:   NASA Space Biology

Dr. Zitnik will serve on the Science Working Group at NASA Space Biology.

Mar 2022:   Yasha's Graduate Research Fellowship

Yasha won the National Defense Science and Engineering Graduate (NDSEG) Fellowship. Congratulations!

Mar 2022:   AI4Science at ICML 2022

We are excited to be selected to organize the AI4Science meeting at ICML 2022. Stay tuned for details.

Mar 2022:   Graph Algorithms in Biomedicine at PSB 2023

Excited to be organizing a session on Graph Algorithms at PSB 2023. Stay tuned for details.

Mar 2022:   Multimodal Learning on Graphs

New preprint! We introduce REMAP, a multimodal AI approach for disease relation extraction and classification. Project website.

Feb 2022:   Explainable Graph AI on the Capitol Hill

Owen has been selected to present our research on explainable biomedical AI to members of the US Congress at the “Posters on the Hill” symposium. Congrats Owen!

Feb 2022:   Graph Neural Networks for Time Series

Hot off the press at ICLR 2022. Check out Raindrop, our graph neural network with unique predictive capability to learn from irregular time series. Project website.

Feb 2022:   Biomedical Graph ML Tutorial Accepted to ISMB

Excited to present a tutorial at ISMB 2022 on graph representation learning for precision medicine. Congratulations, Michelle!

Feb 2022:   Marissa Won the Gates Cambridge Scholarship

Marissa Sumathipala is among the 23 outstanding US scholars selected be part of the 2022 class of Gates Cambridge Scholars at the University of Cambridge. Congratulations, Marissa!

Jan 2022:   Inferring Gene Multifunctionality

Jan 2022:   Deep Graph AI for Time Series Accepted to ICLR

Paper on graph representation learning for time series accepted to ICLR. Congratulations, Xiang!

Jan 2022:   Probing GNN Explainers Accepted to AISTATS

Jan 2022:   Marissa Sumathipala selected as Churchill Scholar

Marissa Sumathipala is selected for the prestigious Churchill Scholarship. Congratulations, Marissa!

Jan 2022:   Therapeutics Data Commons User Meetup

We invite you to join the growing open-science community at the User Group Meetup of Therapeutics Data Commons! Register for the first live user group meeting on Tuesday, January 25 at 11:00 AM EST.

Jan 2022:   Workshop on Graph Learning Benchmarks

Dec 2021:   NASA: Precision Space Health System

Human space exploration beyond low Earth orbit will involve missions of significant distance and duration. To effectively mitigate myriad space health hazards, paradigm shifts in data and space health systems are necessary to enable Earth independence. Delighted to be working with NASA and can share our recommendations!

Dec 2021:   Beyond Low Earth Orbit: Biological Research & AI

As space exploration is extended beyond low Earth orbit, experiments must be maximally autonomous and intelligent to guide biological research. Excited to be working with NASA and can share our recommendations!

Dec 2021:   Attend Our AI4Science Workshop at NeurIPS 2021

Join us at NeurIPS 2021 for the AI for Science Workshop on Monday, Dec 13, 8am-6pm ET. This will be a great day to celebrate AI achievements in scientific discovery and highlight open challenges that need to be addressed to move the field forward.

Nov 2021:   Submit to Our AAAI 2022 Workshop

Submit your finest work at the nexus between trustworthy AI and healthcare to AAAI 2022. Call for papers.

Nov 2021:   Co-Evolution for Functional Interactions

Hot off the press in Nature Communications. Excited to share an ML approach for predicting functional interactions between human genes using the phylogenetic profiles across 1,154 eukaryotic species.

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. Read 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 in PNAS! 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

Zitnik Lab  ·  Harvard  ·  Department of Biomedical Informatics