Precision Medicine Oriented Knowledge Graph

PrimeKG is a precision medicine-oriented knowledge graph that provides a holistic view of diseases. It integrates 20 high-quality resources to describe 17,080 diseases with 5,050,249 relationships representing ten major biological scales, including disease-associated protein perturbations, biological processes and pathways, anatomical and phenotypic scale, and the entire range of approved and experimental drugs with their therapeutic action.

PrimeKG supports drug-disease prediction by including an abundance of ’indications’, ’contradictions’ and ’off-label use’ edges, which are usually missing in other knowledge graphs. We accompany PrimeKG’s graph structure with text descriptions of clinical guidelines for drugs and diseases to enable multi-modal analyses.

View the PrimeKG Website


Evaluating Explainability for Graph Neural Networks

GraphXAI is a resource to systematically evaluate and benchmark the quality of GNN explanations. A key component is a novel and flexible synthetic dataset generator called ShapeGGen that automatically generates a variety of benchmark datasets (e.g., varying graph sizes, degree distributions, homophilic vs. heterophilic graphs) together with ground-truth explanations that address all known pitfalls of explainability methods.

View the GraphXAI Website

Physical Activity Monitoring Dataset

Dataset for Irregular Time Series Research

We are developing representation learning techniques for complex time series dataset. In the Raindrop study (ICLR’22), we introduced a graph-guided network for irregularly sampled multivariate time series. The study includes a processed sensor dataset recording daily living activities of individuals.

View the Physical Activity Monitoring Dataset

Population-Scale Patient Safety Dataset

Adverse Events of Medications across Patient Groups and the Entire Range of Human Diseases and Approved Drugs

We present a comprehensive catalog of 10,443,476 adverse event reports (involving 19,193 adverse events and 3,624 drugs) from the U.S. Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), collected from January 2013 to September 2020. The new resource can help discover relationships between drugs and safety events, especially in cases of rare events and effects within population subgroups that differ in their risks of specific clinical outcomes and are disproportionately affected by preventable inequities.

View the Patient Safety Dataset

Subgraph Datasets

Datasets for Subgraph Representation Learning Research

We design novel synthetic and real-world social and biological datasets, consisting of underlying base graphs and many labeled subgraphs. These datasets are ready to be used for benchmarking, systematic model evaluation and comparison.

View the SubGNN Website

Therapeutics Data Commons

Machine Learning Datasets and Tasks for Drug Discovery and Development

TDC is the first unifying framework to systematically access and evaluate machine learning across the entire range of therapeutics.

At its core, TDC is a collection of AI/ML-ready datasets and learning tasks to serve as a meeting point for domain and ML scientists. TDC also provides an ecosystem of tools, libraries, leaderboards, and community resources, including data functions, strategies for systematic model evaluation, meaningful data splits, data processors, and molecule generation oracles. All datasets and learning tasks are integrated and accessible via an open-source library.

View the TDC Website


Stanford Biomedical Network Dataset Collection

BioSNAP is a collection diverse biomedical networks, inclusing protein-protein interaction networks, single-cell similarity networks, drug-drug interaction networks.

BioSNAP datasets contain metadata on graphs and node features, and can be easily linked to external repositories of biological knowledge.

View the BioSNAP Website

Fair Graph Datasets

Graph datasets comprising of high-stakes decisions in criminal justice and financial lending domains

Graph datasets comprise of critical decisions in criminal justice (if a defendant should be released on bail) and financial lending (if an individual should be given loan) domains. These attributed graphs contain sensitive/protected attributes, which makes them suitable for studying algorithmic fairness.

View the NIFTY website


The Open Graph Benchmark

OGB is a collection of benchmark datasets, data loaders, and evaluators for graph machine learning. Datasets cover a variety of graph machine learning tasks and real-world applications.

The OGB data loaders are fully compatible with popular graph deep learning frameworks, including Pytorch Geometric and DGL. They provide automatic dataset downloading, standardized dataset splits, and unified performance evaluation.

View the OGB Website

Disease pathways

Disease pathways overlaid on the human interactome

View Disease Pathway Dataset

Multimodal cancer network

Multimodal network centered on genes frequently mutated in cancer patients

The multimodal cancer network integrates information on chemicals, diseases, molecular functions, genes, and protein.

The dataset has 21 types of biologically meaningful associations (edge types): chemical-chemical, chemical-protein, disease-chemical, disease-disease, disease-function, disease-gene, function-function, gene-gene (split into 6 edge types by interaction type), gene-protein, protein-function, and protein-protein interactions.

The network has 20 K nodes and 3.4 M edges.

View the Multimodal Cancer Network

Giga-scale biological network

The giga-scale biological network is one of the largest networks ever constructed in biology. The network integrates protein and genetic interaction data from more than two thousand species.

The network has 10 M nodes and 2.3 B edges.

View the Giga-Scale Biological Network

Tree of life

Protein interactomes across the tree of life

The dataset contains protein interactomes from 1,840 species across the tree of life. The dataset contains rich metadata about proteins, including their homology relationships

The dataset also contains metadata about species, including taxonomy of species, phylogenetic reltionships, and ecological information on environments and habitats in which species live.

View the Tree of Life dataset

Polypharmacy network

Network of drugs, proteins, and side effects

The polypharmacy network is a highly multi-relational network, consisting of protein-protein interactions, drug-protein targets, and drug-drug interactions encoded by polypharmacy side effects.

The network has 20 K nodes and 5 M edges, which are split into 1 K distinct edge types.

View the Polypharmacy Network

Tissue-specific protein dataset

The dataset contains protein-protein interaction networks specific to 107 human tissues, a tissue hierarchy of anatomical relationships between tissues, and tissue-specific gene-function annotations.

View the Tissue-Specific Protein Dataset

Human knowledge network

The human knowledge network contains interactions between proteins, diseases, biological processes, side effects, and drugs.

The network has 98 K nodes and 8 M edges, which are split into 42 distinct types of biologically relevant molecular interactions.

View the Human Knowledge Network

Latest News

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.

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.

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