Machine Learning Made Easy (MLme): A Comprehensive Toolkit for Machine Learning-Driven Data Analysis.

Area of Science:

• Bioinformatics
• Computational Biology
• Data Science

Background:

• Machine learning (ML) analysis of complex datasets is crucial but hindered by challenging pipeline development and extensive coding requirements.
• Existing ML tools demand significant expertise in ML principles and programming, impeding research progress.
• Optimizing ML pipeline performance often requires comprehensive user configuration.

Approach:

• Developed Machine Learning Made Easy (MLme), a novel tool integrating Data Exploration, AutoML, CustomML, and Visualization functionalities.
• MLme streamlines ML application in research, focusing on classification tasks and minimizing coding requirements.
• The tool was rigorously tested on six diverse datasets to demonstrate its versatility and effectiveness.

Key Points:

• MLme successfully demonstrated promising performance across various datasets, highlighting its adaptability.
• Feature selection within MLme identified significant cell population markers: BACH2 for CD8+ naive, CD16 for CD16+, and VCAN for CD14+.
• The tool effectively reduces the complexity associated with implementing ML in research.

Conclusions:

• MLme empowers researchers to leverage ML for insightful data analysis and improved research outcomes.
• The tool alleviates the burden of complex coding scripts in ML applications.
• Source code and tutorials for MLme are publicly available for broader adoption and use.