Repository Index

Official repository for the Boltz biomolecular interaction models

• binding-prediction
• drug-discovery
• protein-structure

Graph-linked unified embedding for single-cell multi-omics data integration

• bioinformatics
• deep-learning
• single-cell
• single-cell-multiomics

Benchmarking analysis of data integration tools

Open Frontier Biomolecular Intelligence

MOGONET (Multi-Omics Graph cOnvolutional NETworks) is a novel multi-omics data integrative analysis framework for classification tasks in biomedical applications.

TransformerCPI: Improving compound–protein interaction prediction by sequence-based deep learning with self-attention mechanism and label reversal experiments(BIOINFORMATICS 2020) https://doi.org/10.1093/bioinformatics/btaa524

• self-attention
• compound-protein-interaction
• transformercpi
• structure-free-virtual-sreening

Interpretable bilinear attention network with domain adaptation improves drug-target prediction.

Learning cell communication from spatial graphs of cells

• tensorflow
• gnn
• glm
• cvae

CASSIA: A Multi-Agent LLM-Based Single-Cell Cell Type Annotation Framework

• ai4science
• llm
• multiagent-systems
• prompt-engineering

NeoDTI: Neural integration of neighbor information from a heterogeneous network for discovering new drug-target interactions

• computational-biology
• bioinformatics
• graph-convolution
• deep-learning

PyTorch implementation of the MIDAS algorithm for single-cell multimodal data integration (Nature Biotechnology 2024).

• deep-generative-model
• deep-learning
• single-cell-data-integration
• single-cell-multiomics

MCP server for spatial transcriptomics analysis through natural language interfaces.

• bioinformatics
• cell-communication
• deconvolution
• machine-learning

Multigrate: multiomic data integration for single-cell genomics

Official implementation of JAMIE, Joint variational Autoencoders for Multimodal Imputation and Embedding

• autoencoder
• imputation
• integration
• multimodal

Interpretable modeling of cellular responses to drug perturbations using cycle consistence learning

Vendor-agnostic, FHIR-Genomics-aligned API contract for cross-vendor NGS interpretation. JSON Schema + TypeScript + Python pydantic.