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MarkerML - Marker Feature Identification in Metagenomic Datasets Using Interpretable Machine Learning.

Sunil Nagpal¹, Rohan Singh², Bhupesh Taneja³

¹TCS Research, Tata Consultancy Services Ltd, Pune 411 013, India; CSIR-Institute of Genomics and Integrative Biology (CSIR-IGIB), New Delhi 110 025, India; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201 002, India. Electronic address: https://twitter.com/NagpalSun.

Journal of Molecular Biology

|June 6, 2022

Summary

This summary is machine-generated.

MarkerML identifies environment-specific microbial features using interpretable machine learning. This web server provides contextual insights into feature importance and interdependencies for microbiome analysis.

Keywords:

SHAP interpretable machine learning marker features metagenomic biomarkers microbiome

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Area of Science:

Microbiome research
Metagenomics
Bioinformatics

Background:

Identifying environment-specific marker-features is crucial for microbiome studies.
Conventional methods like hypothesis testing and black-box models lack quantifiable feature relevance and context.

Purpose of the Study:

To introduce MarkerML, a web server utilizing interpretable machine learning for contextual discovery of metagenomic features.
To provide insights into the role and inter-dependence of identified features in machine learning models.

Main Methods:

Application of Shapley Additive Explanations (SHAP) for feature interpretability.
Integration with compositionality-accounted hypothesis testing for multivariate microbiome datasets.
Automated generation of visualizations including prediction effect plots, Sungrams, and violin plots.

Main Results:

MarkerML facilitates the identification of significant marker-features in microbiome data.
The platform offers insights into feature contributions and relationships within machine learning models.
Includes features for bias exclusion and result reproducibility.

Conclusions:

MarkerML enhances microbiome data analysis by providing contextual understanding of marker-features.
The web server serves as a valuable tool for scientists in microbiome research and related fields.
Offers advanced, interpretable analysis beyond traditional methods.