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NFnetFu: A novel workflow for microbiome data fusion.

Vartika Bisht¹, Animesh Acharjee², Georgios V Gkoutos³

¹College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, B15 2TT, UK.

Computers in Biology and Medicine

|July 3, 2021

Summary

Analyzing microbiome data is difficult due to complexity and sparsity. Our new framework, NFnetFU (Neuro Fuzzy network Fusion), offers interpretable models and identifies candidate taxa for validation.

Keywords:

Clustering Fuzzy inference Microbiome Network fusion

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Area of Science:

Microbiology
Bioinformatics
Computational Biology

Background:

Microbiome data analysis presents significant challenges due to factors like collinearity, sparsity, and effect size.
Interpretable models integrating data inferences and biomedical knowledge are crucial for meaningful microbiome insights.

Purpose of the Study:

To develop a multimodal framework addressing key challenges in microbiome data analysis.
To create interpretable models that incorporate background biomedical knowledge.
To identify candidate taxa for future experimental validation.

Main Methods:

Developed a multimodal framework incorporating sparsity, lower effect size, and collinearity.
Utilized a cluster-infused enriched network architecture to integrate background biomedical knowledge.
Implemented the framework in a freely available tool named NFnetFU (Neuro Fuzzy network Fusion).

Main Results:

The framework effectively considers sparsity, low effect size, and microbial correlations (collinearity).
It integrates background biomedical knowledge through a novel network architecture.
The NFnetFU tool identifies candidate taxa/Operational Taxonomic Units (OTUs) for validation.

Conclusions:

The developed NFnetFU framework provides a robust approach to microbiome data analysis.
It enhances interpretability by integrating diverse data types and biomedical knowledge.
NFnetFU facilitates the identification of key microbial targets for further research.