CLASSify: A Web-Based Tool for Machine Learning.

Area of Science:

• Bioinformatics
• Computational Biology
• Machine Learning Applications

Background:

• Machine learning classification is crucial in bioinformatics but requires specialized expertise.
• Researchers often face barriers due to the technical complexity of model training and optimization.
• There is a need for accessible tools to democratize machine learning in biological data analysis.

Purpose of the Study:

• To present CLASSify, an open-source tool designed to automate machine learning classification.
• To simplify the process of model training, optimization, and result interpretation for researchers.
• To provide intuitive visualizations and insights into biological datasets.

Main Methods:

• Development of an automated machine learning classification tool (CLASSify).
• Integration of synthetic data generation for data imputation and balancing.
• Inclusion of feature evaluation and explainability scoring for model interpretability.

Main Results:

• CLASSify supports both binary and multiclass classification tasks.
• The tool offers a variety of machine learning models and methods.
• Synthetic data generation capabilities address missing values and class imbalance.
• Feature evaluation and explainability scores highlight influential features.

Conclusions:

• CLASSify significantly lowers the barrier to entry for machine learning in bioinformatics.
• The tool empowers researchers to perform advanced classification analyses without deep ML expertise.
• CLASSify enhances data understanding through automated analysis and insightful visualizations.