Repository Index

POT : Python Optimal Transport

• optimal-transport
• numerical-optimization
• machine-learning
• emd

Single-cell analysis in Python. Scales to >100M cells.

• machine-learning
• data-science
• visualize-data
• transcriptomics

This package contains deep learning models and related scripts for RoseTTAFold

Spatial Single Cell Analysis in Python

• single-cell-rna-seq
• single-cell-genomics
• spatial-transcriptomics
• spatial-analysis

a molecular descriptor calculator

Tools for computational pathology

• machine-learning
• digital-pathology
• computational-pathology
• biomedical-image-processing

Pathology Language and Image Pre-Training (PLIP) is the first vision and language foundation model for Pathology AI (Nature Medicine). PLIP is a large-scale pre-trained model that can be used to extract visual and language features from pathology images and text description. The model is a fine-tuned version of the original CLIP model.

• artificial-intelligence
• clip
• pathology
• vision-and-language

Descriptor computation(chemistry) and (optional) storage for machine learning

OPEM (Open Source PEM Fuel Cell Simulation Tool)

• chemistry
• pem
• fuel-cell
• opem

A script to run structural alerts using the RDKit and ChEMBL

Run scripts, explore your genome, and get personalized explanations — all in natural dialogue.

• analysis
• claude
• claudecode
• dna

Adaptive Graph Attention Auto-encoder for Spatial Domain Identification of Spatial Transcriptomics

MCP server for spatial transcriptomics analysis through natural language interfaces.

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

Fast, deterministic cell-type deconvolution for million-bin spatial transcriptomics

• bioinformatics
• cell-type-deconvolution
• computational-biology
• deconvolution

Generative models for transcriptomic-driven or protein-driven molecular design (PaccMann^RL).

• deep-reinforcement-learning
• generative-model
• vae
• molecule-generation

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