Automated Statistical and Machine Learning Platform for Biological Research

TL;DR

This paper introduces an integrated platform combining statistical methods and machine learning, specifically Random Forests, to streamline biological data analysis and improve workflow efficiency for researchers.

Contribution

The platform uniquely unifies statistical and machine learning tools with automated features, making advanced analysis accessible without extensive programming skills.

Findings

Effective classification accuracy across diverse datasets

Automated hyperparameter optimization improves model performance

Unified interface enhances workflow efficiency

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.