Recent News


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Jan 2023:   GNNDelete at ICLR 2023

Jan 2023:   New Network Principle for Molecular Phenotypes

Dec 2022:   Can we shorten rare disease diagnostic odyssey?

New preprint! Geometric deep learning for diagnosing patients with rare genetic diseases. Implications for using deep learning on sparsely-labeled medical datasets. Thankful for this collaboration with Zak Lab. Project website.

Nov 2022:   Can AI transform the way we discover new drugs?

Our conversation with Harvard Medicine News highlights recent developments and new features in Therapeutics Data Commons.

Oct 2022:   New Paper in Nature Biomedical Engineering

New paper on graph representation learning in biomedicine and healthcare published in Nature Biomedical Engineering.

Sep 2022:   New Paper in Nature Chemical Biology

Our paper on artificial intelligence foundation for therapeutic science is published in Nature Chemical Biology.

Sep 2022:   Self-Supervised Pre-Training at NeurIPS 2022

New paper on self-supervised contrastive pre-training accepted at NeurIPS 2022. Project page. Thankful for this collaboration with the Lincoln National Laboratory.

Sep 2022:   Best Paper Honorable Mention Award at IEEE VIS

Our paper on user-centric AI of drug repurposing received the Best Paper Honorable Mention Award at IEEE VIS 2022. Thankful for this collaboration with Gehlenborg Lab.

Sep 2022:   Multimodal Representation Learning with Graphs

Aug 2022:   On Graph AI for Precision Medicine

The recording of our tutorial on using graph AI to advance precision medicine is available. Tune into four hours of interactive lectures about state-of-the-art graph AI methods and applications in precision medicine.

Aug 2022:   Evaluating Explainability for GNNs

New preprint! We introduce a resource for broad evaluation of the quality and reliability of GNN explanations, addressing challenges and providing solutions for GNN explainability. Project website.

Jul 2022:   New Frontiers in Graph Learning at NeurIPS

Excited to organize the New Frontiers in Graph Learning workshop at NeurIPS.

Jul 2022:   AI4Science at NeurIPS

We are excited to host the AI4Science meeting at NeurIPS discussing AI-driven scientific discovery, implementation and verification of AI in science, the influence AI has on the conduct of science, and more.

Jul 2022:   Graph AI for Precision Medicine at ISMB

Jul 2022:   Welcoming Fellows and Summer Students

Welcoming a research fellow Julia Balla and three Summer students, Nicholas Ho, Satvik Tripathi, and Isuru Herath.

Jun 2022:   Broadly Generalizable Pre-Training Approach

Excited to share a preprint on self-supervised method for pre-training. Project website with evaluation on eight datasets, including electrodiagnostic testing, human daily activity recognition, and health state monitoring.

Jun 2022:   Welcoming New Postdocs

Excited to welcome George Dasoulas and Huan He, new postdocs joining us this Summer.

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.

May 2022:   Building KGs to Support Precision Medicine

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. http://www.ai4science.net/icml22

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.

Zitnik Lab  ·  Artificial Intelligence in Medicine and Science  ·  Harvard  ·  Department of Biomedical Informatics