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Updated: Jun 4, 2025

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

A Clinical Metaproteomics Workflow Implemented within Galaxy Bioinformatics Platform to Analyze Host-Microbiome Interactions Underlying Human Disease

Published on: January 10, 2025

Multitask knowledge-primed neural network for predicting missing metadata and host phenotype based on human

Mahsa Monshizadeh¹, Yuhui Hong¹, Yuzhen Ye¹

¹Computer Science Department, Indiana University, Bloomington, IN 47408, United States.

Bioinformatics Advances

|December 30, 2024

Summary

This summary is machine-generated.

This study introduces MicroKPNN-MT, a machine learning model that improves human disease prediction using microbiome data and host metadata. The model enhances accuracy and generalizability by integrating or predicting metadata, aiding microbiome-based health insights.

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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

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Last Updated: Jun 4, 2025

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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Area of Science:

Microbiome research
Computational biology
Machine learning in health

Background:

Microbiome signatures are linked to human diseases, prompting machine learning for prediction.
Challenges exist in accuracy, generalizability, and interpretability of microbiome-based predictions.
Host factors like age and gender confound microbiome analysis and predictions.

Purpose of the Study:

To develop a unified model, MicroKPNN-MT, for predicting human phenotype from microbiome data and metadata.
To enhance prediction accuracy and generalizability by incorporating host metadata.
To predict missing metadata from microbiome data, improving model robustness.

Main Methods:

Developed MicroKPNN-MT, an extension of the MicroKPNN framework.
Integrated host metadata (age, gender) as input features.
Utilized additional decoders to predict metadata from microbiome data when unavailable.
Applied the model to the mBodyMap dataset covering healthy individuals and 25 diseases.

Main Results:

MicroKPNN-MT demonstrated potential as a predictive tool for multiple diseases.
The model successfully predicted missing metadata from microbiome data.
Incorporating real or predicted metadata significantly improved disease prediction accuracy.
Enhanced generalizability of predictive models by leveraging metadata.

Conclusions:

MicroKPNN-MT offers a unified approach for microbiome-based disease and metadata prediction.
The integration or prediction of host metadata is crucial for robust microbiome-based health predictions.
This model advances the application of machine learning in understanding the human microbiome's role in health and disease.