OmicsMLMentor: A Web Application for Guided Machine Learning Analysis of Omics Data.

Area of Science:

• Bioinformatics
• Computational Biology
• Data Science

Background:

• Omics technologies generate complex datasets requiring advanced analytical methods.
• Machine learning (ML) models are crucial for interpreting omics data but pose challenges for researchers.
• Existing tools often lack specialized features for omics data preprocessing and model selection.

Purpose of the Study:

• To introduce OmicsMLMentor, an open-source web application designed to simplify ML modeling for omics data.
• To provide omics researchers with accessible tools for data preprocessing, model fitting, and hyperparameter optimization.
• To facilitate the broader adoption of ML in omics research by lowering technical barriers.

Main Methods:

• OmicsMLMentor supports 15 ML models (10 supervised, 5 unsupervised) tailored for omics datasets.
• Features include methods for handling missing data (imputation, removal), normalization testing, and model ranking.
• Cross-validation is used for optimal hyperparameter selection and model performance evaluation.

Main Results:

• OmicsMLMentor successfully streamlines ML workflows for omics data analysis.
• The application addresses critical gaps in existing web tools for omics data interpretation.
• Demonstrated application to a lignin exposure study highlights practical workflows for supervised and unsupervised models.

Conclusions:

• OmicsMLMentor significantly lowers the barrier to entry for applying ML to omics data.
• The tool empowers researchers to perform sophisticated analyses without extensive statistical programming expertise.
• Facilitates deeper insights from omics data, advancing fields like proteomics, metabolomics, and transcriptomics.