Back

MarkerScout: A Disease-Agnostic Machine Learning Framework for Biomarker Prediction from Multi-Scale Mechanistic Models

Moore, R. B.; Agayie-Ntim, F.; Crawford, L. B.; Packrisamy, P.; Hamed, A. A.; Helikar, T.

2026-06-05 bioinformatics
10.64898/2026.05.30.727266 bioRxiv
Show abstract

Identifying robust biomarkers from high-dimensional biomedical data is a central challenge in translational research, but candidate rankings produced by any single feature-selection or classification method depend on algorithmic choices and rarely reproduce across pipelines. We present a disease-agnostic machine-learning framework that addresses this dependence by systematically benchmarking 25 (feature-selection x classifier) pipelines under five-fold stratified cross-validation, aggregating per-feature evidence by two independent methods (a weighted-selection consensus score and Robust Rank Aggregation), and characterizing the direction of each candidate using Cohens d. We demonstrate the framework on immune-response measurements from two clinical phases: SARS-CoV-2 hospitalization and intensive-care admission; obtaining cross-validated mean F1 above 0.99 with balanced classification errors and producing tiered, direction-aware biomarker lists per phase. Interleukin-18 (IL-18) reached the strongest tier in both phases with consistent direction. The framework generalizes to any binary clinical classification problem and supports principled, reproducible biomarker prioritization.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Patterns
○ 78 papers in training set
Top 0.1%
11.6%
Similar papers in this journal
2
Nature Communications
○ 5641 papers in training set
Top 19%
9.4%
Similar papers in this journal
3
npj Digital Medicine
○ 118 papers in training set
Top 0.8%
7.7%
Similar papers in this journal
4
Briefings in Bioinformatics
◐ 354 papers in training set
Top 1%
6.6%
Similar papers in this journal
5
Scientific Reports
○ 3612 papers in training set
Top 13%
6.1%
Similar papers in this journal
6
Genome Medicine
○ 183 papers in training set
Top 0.7%
5.0%
7
Nature Machine Intelligence
○ 70 papers in training set
Top 0.5%
5.0%
50% of probability mass above
8
Cell Systems
○ 201 papers in training set
Top 0.9%
4.7%
9
Cell Reports Medicine
○ 153 papers in training set
Top 0.6%
4.2%
10
PLOS Computational Biology
● 1863 papers in training set
Top 10%
3.2%
11
Molecular Systems Biology
○ 162 papers in training set
Top 0.8%
2.7%
12
npj Systems Biology and Applications
○ 125 papers in training set
Top 0.6%
2.7%
13
BMC Bioinformatics
○ 457 papers in training set
Top 3%
2.1%
14
Frontiers in Immunology
○ 638 papers in training set
Top 5%
2.0%
15
iScience
○ 1154 papers in training set
Top 19%
1.7%
16
PLOS ONE
● 5266 papers in training set
Top 49%
1.5%
17
Computers in Biology and Medicine
○ 128 papers in training set
Top 3%
1.4%
18
Bioinformatics
◐ 1204 papers in training set
Top 7%
1.3%
19
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 5%
1.1%
20
The Lancet Digital Health
○ 25 papers in training set
Top 0.6%
1.1%
21
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 4%
1.0%
22
Journal of Translational Medicine
○ 57 papers in training set
Top 2%
1.0%
23
Communications Biology
○ 993 papers in training set
Top 27%
1.0%
24
Science Advances
● 1243 papers in training set
Top 29%
0.9%
25
BioData Mining
○ 22 papers in training set
Top 0.7%
0.8%
26
eBioMedicine
○ 183 papers in training set
Top 7%
0.8%
27
Nucleic Acids Research
◐ 1281 papers in training set
Top 15%
0.6%
28
Communications Medicine
○ 113 papers in training set
Top 5%
0.6%
29
Frontiers in Bioinformatics
○ 49 papers in training set
Top 2%
0.6%
30
eLife
● 5828 papers in training set
Top 71%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.