Advancing Digital Precision Medicine for Chronic Fatigue Syndrome through Longitudinal Large-Scale Multi-Modal Biological Omics Modeling with Machine Learning and Artificial Intelligence

TL;DR

This paper introduces BioMapAI, an explainable deep learning framework that leverages longitudinal multi-omics data to improve diagnosis and understanding of chronic fatigue syndrome and similar diseases.

Contribution

It presents a novel multi-omics modeling approach with explainability, biomarker discovery, and disease classification for ME/CFS using large-scale longitudinal data.

Findings

BioMapAI achieved state-of-the-art disease classification accuracy.

Identified disease- and symptom-specific biomarkers.

Mapped microbiome-immune-metabolome crosstalk shifts in ME/CFS.

Abstract

We studied a generalized question: chronic diseases like ME/CFS and long COVID exhibit high heterogeneity with multifactorial etiology and progression, complicating diagnosis and treatment. To address this, we developed BioMapAI, an explainable Deep Learning framework using the richest longitudinal multi-omics dataset for ME/CFS to date. This dataset includes gut metagenomics, plasma metabolome, immune profiling, blood labs, and clinical symptoms. By connecting multi-omics to a symptom matrix, BioMapAI identified both disease- and symptom-specific biomarkers, reconstructed symptoms, and achieved state-of-the-art precision in disease classification. We also created the first connectivity map of these omics in both healthy and disease states and revealed how microbiome-immune-metabolome crosstalk shifted from healthy to ME/CFS.