Open Source Tools


Our team on GitHub

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

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!

Zitnik Lab  ·  Harvard  ·  Department of Biomedical Informatics