Open Source Data
BioSNAP
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.
OGB
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.
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.
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.
Latest News
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
A new preprint on discovering disease treatment mechanisms through the 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!
Mar 2020: Accepted Tutorial at IJCAI!
We will present a tutorial on Machine Learning for Drug Development at IJCAI 2020! Stay tuned for details.
Mar 2020: Welcome New Students!
Haoxin, Michelle, and Xiang joined the lab. Welcome! We look forward to seeing you all in the lab!
Feb 2020: Meta Learning for Single-cell Biology
A new preprint on meta learning for identifying and naming cell types, even cell types that have never been seen before and do not exist in the training data. Check it out!
Dec 2019: Pre-training Graph Neural Networks
Strategies for pre-training graph neural networks accepted as a spotlight paper at ICLR 2020.
Dec 2019: Deep Learning for Network Biology
Marinka is co-editing a special issue of ACM/IEEE TCBB on Deep learning and graph embeddings for network biology. Submit your finest work!
Tweets