Open Source Data
Therapeutics Data Commons
Machine Learning Datasets and Tasks for Therapeutics
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.
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.
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.
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.
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.
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.
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 prrteins, 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.
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.
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.
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.
Our short paper on Interactive Visual Explanations for Deep Drug Repurposing received the Best Paper Award at ICML 2021 Interpretable ML in Healthcare Workshop. Stay tuned for more news on this evolving project.
We are excited to be at ICML 2021 where we will present 1 paper at Workshop on Socially Responsible Machine Learning, 1 paper at Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI, 2 papers at Workshop on Interpretable Machine Learning in Healthcare, and 1 paper at Workshop on Computational Biology. Congratulations to fantastic students!
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.
New preprint on contextualized protein embeddings aims to characterize genes with disease-specific interactions and elucidate disease manifestation in specific cell types.
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.
Hot off the press! 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.
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.
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.
Michelle M. Li won the NSF Graduate Research Fellowship Award. Congratulations!
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.
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.
We are excited to share the preprint on Therapeutics Data Commons: Machine Learning Datasets and Tasks for Therapeutics. TDC is available at http://tdcommons.ai.
The new algorithmic approach investigates population-scale patient safety data and reveals inequalities in adverse events before and during COVID-19 pandemic.
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.
We are excited to see published our paper on DeepPurpose, a deep learning library for drug-target interaction prediction, and our paper on skipGNN, a graph neural network for predicting molecular interactions.
Our research won the Bayer Early Excellence in Science Award. We are honored to have received this recognition!
We are thrilled to announce Therapeutics Data Commons (TDC)! We invite you to join TDC. TDC is an open-source and community-driven effort.
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.