MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models
Abstract
We demonstrate the framework on three infectious diseases derived from a companion mechanistic immune-simulation platform: SARS-CoV-2, Influenza A Virus, and Plasmodium falciparum. Each disease was evaluated across hospitalization and intensive care unit cohorts, yielding six cohorts in total. Best-pipeline cross-validated macro F1 ranged from 0.82 for IAV-HOSP to 0.99 for COV-ICU, and the framework produced tiered, direction-aware biomarker lists for each disease and phase. Interleukin-18 (IL-18) reached the strongest tier in both SARS-CoV-2 phases with consistent direction. When benchmarked against three separate, independently collected clinical ICU datasets, MarkerScout's top-ranked features outperformed 94.4% of randomly selected feature sets of equivalent size for SARS-CoV-2, with a weaker but directionally consistent advantage for Influenza A Virus (66.7%) and Plasmodium falciparum (60.7%).
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BibTeX
@misc{moore2026markerscout,
title = {{MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models}},
author = {Robert Moore and Frank Agayie-Ntim and Lindsey B. Crawford and M. Jana Broadhurst and David M. Brett-Major and Prakash Packrisamy and Ahmed Abdeen Hamed and Tomas Helikar},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.04268},
eprint = {2609.04268},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.04268},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models
AU - Robert Moore
AU - Frank Agayie-Ntim
AU - Lindsey B. Crawford
AU - M. Jana Broadhurst
AU - David M. Brett-Major
AU - Prakash Packrisamy
AU - Ahmed Abdeen Hamed
AU - Tomas Helikar
PY - 2026
DA - 2026/09/02
PB - arXiv
DO - 10.48550/arxiv.2609.04268
UR - https://arxiv.org/abs/2609.04268
AB - We demonstrate the framework on three infectious diseases derived from a companion mechanistic immune-simulation platform: SARS-CoV-2, Influenza A Virus, and Plasmodium falciparum. Each disease was evaluated across hospitalization and intensive care unit cohorts, yielding six cohorts in total. Best-pipeline cross-validated macro F1 ranged from 0.82 for IAV-HOSP to 0.99 for COV-ICU, and the framework produced tiered, direction-aware biomarker lists for each disease and phase. Interleukin-18 (IL-18) reached the strongest tier in both SARS-CoV-2 phases with consistent direction. When benchmarked against three separate, independently collected clinical ICU datasets, MarkerScout's top-ranked features outperformed 94.4% of randomly selected feature sets of equivalent size for SARS-CoV-2, with a weaker but directionally consistent advantage for Influenza A Virus (66.7%) and Plasmodium falciparum (60.7%).
ER -