Open Source Tools


Our team on GitHub

Therapeutics Data Commons

Machine Learning Datasets and Tasks for Therapeutics

View TDC TDC Website

G-Meta

Graph meta learning via local subgraphs

View G-Meta G-Meta Project Website

SubGNN

Subgraph Neural Networks

View SubGNN SubGNN Project Website

GNNGuard

Defending graph neural networks against adversarial attacks

View GNNGuard GNNGuard Project Website

Graph ML Tutorials

Tutorials on machine learning for graphs

View Graph ML Tutorials

Nimfa

Python module for fast non-negative matrix factorization

View Nimfa Nimfa Project Website

Decagon

Graph neural networks for multirelational link prediction

View Decagon

DeepPurpose

Deep learning library for drug-target interaction prediction and applications to drug repurposing and virtual screening

View DeepPurpose

SkipGNN

Skip-graph networks for molecular interaction prediction

View SkipGNN

scikit-fusion

Data fusion via collective latent factor models

View Scikit-fusion

Network Enhancement

Method for denoising biological networks

View NE

CRank

Method for prioritizing network communities

View CRank

OhmNet

Representation learning for multi-layer graphs

View OhmNet

Mambo

Tool for construction, representation, and analysis of large multi-modal networks

View Mambo

GNNExplainer

Method for generating explanations for graph neural networks

View GNNExplainer

GraphWave

Method for learning structural node embeddings

View GraphWave

Graph Query Embeddings

Method for embedding logical queries on knowledge graphs

View Graph Query Embeddings

Collage

Method for gene prioritization by compressive data fusion and chaining

View Collage

Network-Guided Matrix Completion

Method for probabilistic prediction and imputation of interactions using prior knowledge

View NGMC

fast-NMTF

Fast methods for non-negative matrix tri-factorization

View Fast-NMTF

Multi-BioNER

Deep multi-task learning for cross-type biomedical named entity recognition

View Multi-BioNER

CROW

Scalable multi-GPU and multi-CPU methods for non-negative matrix tri-factorization

View CROW

Latest News

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.

Zitnik Lab  ·  Harvard  ·  Department of Biomedical Informatics