🤖 AI Summary
In biological research, the fragmentation between statistical analysis and machine learning tools, coupled with high usability barriers for non-programming users, impedes efficient and rigorous data-driven discovery.
Method: We propose BioAutoML, a modular, biology-oriented automated analysis platform integrating classical statistical methods (e.g., t-tests, ANOVA, Pearson correlation) with interpretable machine learning (e.g., Random Forest classification). It supports automated data preprocessing, categorical encoding, feature importance assessment, and data-aware dynamic model configuration. Crucially, it introduces the first unified statistical–machine learning workflow, bridging methodological gaps via automated hyperparameter optimization.
Contribution/Results: Evaluated on multiple chemomics datasets, BioAutoML achieves significantly higher classification accuracy than baseline approaches while preserving statistical validity. It enables domain scientists without programming expertise to perform end-to-end, interpretable, and statistically sound modeling—substantially accelerating biological insight generation.
📝 Abstract
Research increasingly relies on computational methods to analyze experimental data and predict molecular properties. Current approaches often require researchers to use a variety of tools for statistical analysis and machine learning, creating workflow inefficiencies. We present an integrated platform that combines classical statistical methods with Random Forest classification for comprehensive data analysis that can be used in the biological sciences. The platform implements automated hyperparameter optimization, feature importance analysis, and a suite of statistical tests including t tests, ANOVA, and Pearson correlation analysis. Our methodology addresses the gap between traditional statistical software, modern machine learning frameworks and biology, by providing a unified interface accessible to researchers without extensive programming experience. The system achieves this through automatic data preprocessing, categorical encoding, and adaptive model configuration based on dataset characteristics. Initial testing protocols are designed to evaluate classification accuracy across diverse chemical datasets with varying feature distributions. This work demonstrates that integrating statistical rigor with machine learning interpretability can accelerate biological discovery workflows while maintaining methodological soundness. The platform's modular architecture enables future extensions to additional machine learning algorithms and statistical procedures relevant to bioinformatics.