We are pleased to announce the official release of Helix, an advanced open-source software platform designed to streamline advanced data analysis and machine learning (ML) workflows for tabular data. Built with extensibility, adaptability, and scientific rigour in mind, Helix provides a modular framework for data scientists, engineers, and researchers working with structured datasets.
GitHub Repository: https://github.com/Biomaterials-for-Medical-Devices-AI/Helix
Documentation: https://biomaterials-for-medical-devices-ai.github.io/Helix/devs/index.html
DOI: 10.5281/zenodo.15351701
Key Features:
- Domain-Agnostic Design: Optimised for a wide array of use cases, including biomedical datasets, quantitative structure–activity relationship (QSAR) modelling for biomaterials, and general engineering data pipelines.
- AI/ML Integration: Supports end-to-end ML workflows encompassing data ingestion, preprocessing, model training, evaluation, post-training interpretation and deployment.
- Extensibility and Modularity: Designed for flexibility, the framework allows seamless incorporation of custom modules, making it suitable for bespoke research and industrial applications.
- Open Source and Community-Driven: Released under an MIT licence, Helix invites collaboration and contributions from the global scientific and developer communities.
Developed as part of the Materials for Medical Devices grant, Helix addresses the critical need for reproducible and interpretable ML workflows in handling complex tabular data. Whether for classification, regression, feature engineering, or exploratory data analysis, the platform empowers users to build robust, scalable models with minimal overhead.
Helix is now freely available via GitHub. Comprehensive documentation and examples are provided to facilitate immediate adoption.
An article covering the software can be found on the HDR UK website.
Posted on Wednesday 7th May 2025