Helix 1.0: An open-source framework for reproducible and interpretable machine learning on tabular scientific data.
other · Level V
Where this comes from
- Record sourced from PubMed, PMID 42130947.
- Also identified by DOI 10.1016/j.patter.2026.101536 and PMC identifier 13161695.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Helix is an open-source, extensible, Python-based software framework to facilitate reproducible and interpretable machine learning workflows for tabular data. It addresses the growing need for transparent experimental data analytics provenance, ensuring that the entire analytical process-including decisions around data transformation and methodological choices-is documented, accessible, reproducible, and comprehensible to relevant stakeholders. The platform comprises modules for standardized data preprocessing, visualization, machine learning model training, evaluation, interpretation, results inspection, and model prediction for unseen data. To further empower researchers without formal training in data science to derive meaningful and actionable insights, Helix features a user-friendly interface that enables the design of computational experiments and inspection of outcomes, including a novel interpretation approach to machine learning decisions using linguistic terms all within an integrated environment.