Helix 1.0: An open-source framework for reproducible and interpretable machine learning on tabular scientific data.

Area of Science:

• Computer Science
• Data Science
• Bioinformatics

Background:

• Increasing complexity of machine learning (ML) workflows.
• Need for transparency and reproducibility in data analytics.
• Challenges for researchers without formal data science training.

Purpose of the Study:

• Introduce Helix, an open-source Python framework for ML.
• Facilitate reproducible and interpretable ML workflows for tabular data.
• Enhance transparency in experimental data analytics provenance.

Main Methods:

• Developed a modular, extensible Python software framework.
• Integrated modules for data preprocessing, visualization, ML training, and evaluation.
• Incorporated a user-friendly interface and novel linguistic interpretation approach.

Main Results:

• Helix provides standardized modules for the entire ML pipeline.
• Enables documentation, accessibility, and reproducibility of analytical processes.
• Offers a novel interpretation approach using linguistic terms for ML decisions.

Conclusions:

• Helix empowers researchers to derive actionable insights from tabular data.
• Improves transparency and interpretability of machine learning workflows.
• Facilitates reproducible computational experiments within an integrated environment.