Helical
Unified framework for state-of-the-art pre-trained bio foundation models across genomics and transcriptomics, providing standardized interfaces and pipelines for DNA, RNA, and single-cell models including Evo 2, Geneformer, scGPT, and UCE with streamlined inference, benchmarking, and fine-tuning workflows (213+ stars, 2024-2025)
README
What is Helical ? Helical builds the Virtual AI Lab for Biological Discovery. This open framework provides access to state-of-the-art Bio Foundation Models across genomics, transcriptomics, and single-cell data modalities. Helical simplifies the entire lifecycle of applying Bio Foundation Models — from model access to fine-tuning and in-silico experimentation. With Helical's open-source framework, you can: • Leverage the latest Bio Foundation Models through a simple Python interface • Run…
- Repository
- github.com/helicalai/helical
Source attribution
- GitHub — github.com/helicalai/helical
- Awesome AI for Science — github.com/helicalai/helical
Related resources
First architecture deeply integrating a DNA foundation model with an LLM for multimodal biological reasoning, achieving 98% accuracy on KEGG disease pathway prediction and 15%+ average gains on variant effect prediction with interpretable step-by-step reasoning traces (bowang-lab, 390+ stars)
Deep learning library for Chemistry based on Tensorflow
Provides functionality for producing geometric representations of protein and RNA structures, and biological interaction networks.
Unified Python framework for bulk, single-cell, and spatial RNA-seq multi-omics analysis with deep learning deconvolution (VAE) and graph neural networks, bridging Bindea, Bindea, scanpy and squidpy ecosystems (Nature Communications 2024)
A client to simplify fetching predictions from the Koina web service. Koina is a model repository enabling the remote execution of models. Predictions are generated as a response to HTTP/S requests, the standard protocol used for nearly all web traffic.
This ontology models classes and relationships describing deep learning networks, their component layers and activation functions, as well as potential biases.