Getting Started with Machine Learning for Experimental Biochemists and Other Molecular Scientists.

Area of Science:

• Experimental molecular science
• Biochemistry
• Cell biology
• Drug discovery

Background:

• Machine learning (ML) is increasingly vital for analyzing complex data in experimental sciences.
• Historically, ML required specialized statistical or cheminformatics training, limiting accessibility for many scientists.
• There is a growing need to democratize ML tools for broader adoption by experimental researchers.

Purpose of the Study:

• To provide accessible, step-by-step protocols for four key ML methods: hierarchical clustering, principal component analysis (PCA), partial least squares discriminant analysis (PLSDA), and partial least squares regression (PLSR).
• To lower the barrier of entry for experimental scientists lacking computer science or statistics backgrounds.
• To enable researchers to effectively use ML for experimental design, hypothesis testing, and data analysis.

Main Methods:

• Detailed protocols for hierarchical clustering, PCA, PLSDA, and PLSR using MATLAB.
• Explanations tailored for users with no prior ML or coding experience.
• Guidance on data selection, scaling, and aligning methods with scientific questions.

Main Results:

• The protocols are designed for ease of use, requiring no prior MATLAB or coding knowledge.
• Emphasis is placed on understanding the relationship between scientific inquiry and appropriate ML methodology.
• The methods covered are particularly relevant for applications in biochemistry, cell biology, and drug discovery.

Conclusions:

• Experimental scientists can now readily apply powerful ML techniques to their research.
• These protocols facilitate data-driven hypothesis testing and experimental design in molecular sciences.
• Broader access to ML empowers a new generation of researchers in chemical biology, chemistry, and biomedical fields.