ProCyon

ProCyon is a groundbreaking foundation model for modeling, generating, and predicting protein phenotypes.

Unified Clinical Vocabulary Embeddings

Unified Clinical Vocabulary Embeddings for Advancing Precision Medicine

Madrigal

Multimodal AI Predicts Clinical Outcomes of Drug Combinations from Preclinical Data

KGARevion

Knowledge Graph Based Agent for Complex, Knowledge-Intensive QA in Medicine

PocketFlow

Generalized Protein Pocket Generation with Prior-Informed Flow Matching

Therapeutics Data Commons 2.0

Multimodal Foundation for Therapeutic Science

SPECTRA

Evaluating Generalizability of Artificial Intelligence Models for Molecular Datasets

PocketGen

Efficient Generation of Protein Pockets with PocketGen

UniTS

A Unified Multi-Task Time Series Model

PDGrapher

Combinatorial Prediction of Therapeutic Perturbations Using Causally-Inspired Neural Networks

FAIR

Full-Atom Protein Pocket Design via Iterative Refinement

TxGNN

A Foundation Model for Clinician Centered Drug Repurposing

PINNACLE

Contextual AI Models for Single-Cell Protein Biology

TimeX

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

Raincoat

Domain Adaptation for Time Series Under Feature and Label Shifts

SHEPHERD

Deep Learning for Diagnosing Patients with Rare Genetic Diseases

GNNDelete

General Strategy for Unlearning in Graph Neural Networks

TF-C

Self-Supervised Contrastive Pre-Training For Time Series

metapaths

Similarity Search in Heterogeneous Knowledge Graphs via Meta Paths

Mutual Interactors

Phenotype Discovery in Molecular Interaction Networks

Raindrop

Graph-Guided Network for Irregularly Sampled Multivariate Time Series

SIPT

Structure Inducing Pre-Training

REMAP

Multimodal Learning on Graphs for Disease Relation Extraction

Therapeutics Data Commons

Machine Learning Datasets and Tasks for Drug Discovery and Development

GraphXAI

Evaluating Explainability for Graph Neural Networks

NIFTY

Unified Framework for Fair and Stable Graph Representation Learning

G-Meta

Graph meta learning via local subgraphs

SubGNN

Subgraph Neural Networks

GNNGuard

Defending graph neural networks against adversarial attacks

Graph ML Tutorials

Tutorials on machine learning for graphs

Nimfa

Python module for fast non-negative matrix factorization

Decagon

Graph neural networks for multirelational link prediction

DeepPurpose

Deep learning library for drug-target interaction prediction and applications to drug repurposing and virtual screening

SkipGNN

Skip-graph networks for molecular interaction prediction

scikit-fusion

Data fusion via collective latent factor models

Network Enhancement

Method for denoising biological networks

CRank

Method for prioritizing network communities

OhmNet

Representation learning for multi-layer graphs

Mambo

Tool for construction, representation, and analysis of large multi-modal networks

GNNExplainer

Method for generating explanations for graph neural networks

GraphWave

Method for learning structural node embeddings

Graph Query Embeddings

Method for embedding logical queries on knowledge graphs

Collage

Method for gene prioritization by compressive data fusion and chaining

Network-Guided Matrix Completion

Method for probabilistic prediction and imputation of interactions using prior knowledge

fast-NMTF

Fast methods for non-negative matrix tri-factorization

Multi-BioNER

Deep multi-task learning for cross-type biomedical named entity recognition

CROW

Scalable multi-GPU and multi-CPU methods for non-negative matrix tri-factorization