Artificial Intelligence & Machine Learning Methods


Our team on GitHub

TF-C

Self-Supervised Contrastive Pre-Training For Time Series

View TF-C TF-C Website

metapaths

Similarity Search in Heterogeneous Knowledge Graphs via Meta Paths

View metapaths metapaths Website metapaths Package

Mutual Interactors

Phenotype Discovery in Molecular Interaction Networks

View Mutual Interactors Mutual Interactors Website

Raindrop

Graph-Guided Network for Irregularly Sampled Multivariate Time Series

View Raindrop Raindrop Website

SIPT

Structure Inducing Pre-Training

View SIPT SIPT Website

REMAP

Multimodal Learning on Graphs for Disease Relation Extraction

View REMAP REMAP Website

Therapeutics Data Commons

Machine Learning Datasets and Tasks for Drug Discovery and Development

View TDC TDC Documentation TDC Website

GraphXAI

Evaluating Explainability for Graph Neural Networks

View GraphXAI GraphXAI Website

NIFTY

Unified Framework for Fair and Stable Graph Representation Learning

View NIFTY NIFTY Website

G-Meta

Graph meta learning via local subgraphs

View G-Meta G-Meta Website

SubGNN

Subgraph Neural Networks

View SubGNN SubGNN Website

GNNGuard

Defending graph neural networks against adversarial attacks

View GNNGuard GNNGuard 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 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

Jan 2023:   GNNDelete at ICLR 2023

Jan 2023:   New Network Principle for Molecular Phenotypes

Dec 2022:   Can we shorten rare disease diagnostic odyssey?

New preprint! Geometric deep learning for diagnosing patients with rare genetic diseases. Implications for using deep learning on sparsely-labeled medical datasets. Thankful for this collaboration with Zak Lab. Project website.

Nov 2022:   Can AI transform the way we discover new drugs?

Our conversation with Harvard Medicine News highlights recent developments and new features in Therapeutics Data Commons.

Oct 2022:   New Paper in Nature Biomedical Engineering

New paper on graph representation learning in biomedicine and healthcare published in Nature Biomedical Engineering.

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

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