Artificial Intelligence & Machine Learning Methods


Our GitHub Spaces

TxGNN

Zero-Shot Prediction of Therapeutic Use with Geometric Deep Learning and Clinician Centered Design

View TxGNN TxGNN Website TxGNN Explorer

AWARE

Contextualizing Protein Representations Using Deep Learning on Interactomes and Single-Cell Experiments

View AWARE AWARE Website

TimeX

Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency

View TimeX TimeX Website

Raincoat

Domain Adaptation for Time Series Under Feature and Label Shifts

View Raincoat Raincoat Website

SHEPHERD

Deep Learning for Diagnosing Patients with Rare Genetic Diseases

View SHEPHERD SHEPHERD Website

GNNDelete

General Strategy for Unlearning in Graph Neural Networks

View GNNDelete GNNDelete Website

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

May 2023:   Congratulations to Ada and Michelle

Congrats to PhD student Michelle on being selected as the 2023 Albert J. Ryan Fellow and also to participate in the Heidelberg Laureate Forum. Congratulations to PhD student Ada for being selected as the Kempner Institute Graduate Fellow!

Apr 2023:   Universal Domain Adaptation at ICML 2023

New paper introducing the first model for closed-set and universal domain adaptation on time series accepted at ICML 2023. Raincoat addresses feature and label shifts and can detect private labels. Project website.

Apr 2023:   Celebrating Achievements of Our Undergrads

Undergraduate researchers Ziyuan, Nick, Yepeng, Jiali, Julia, and Marissa are moving onto their PhD research in Computer Science, Systems Biology, Neuroscience, and Biological & Medical Sciences at Harvard, MIT, Carnegie Mellon University, and UMass Lowell. We are excited for the bright future they created for themselves.

Apr 2023:   Welcoming a New Postdoctoral Fellow

An enthusiastic welcome to Tianlong Chen, our newly appointed postdoctoral fellow.

Apr 2023:   New Study in Nature Machine Intelligence

New paper in Nature Machine Intelligence introducing the blueprint for multimodal learning with graphs.

Mar 2023:   Precision Health in Nature Machine Intelligence

New paper with NASA in Nature Machine Intelligence on biomonitoring and precision health in deep space supported by artificial intelligence.

Mar 2023:   Self-Driving Labs in Nature Machine Intelligence

Mar 2023:   TxGNN - Zero-shot prediction of therapeutic use

Mar 2023:   GraphXAI published in Scientific Data

Feb 2023:   Welcoming New Postdoctoral Fellows

A warm welcome to postdoctoral fellows Wanxiang Shen and Ruth Johnson. Congratulations to Ruthie for being named a Berkowitz Fellow.

Feb 2023:   New Preprint on Distribution Shifts

Feb 2023:   PrimeKG published in Scientific Data

Jan 2023:   GNNDelete published 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.

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