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Exploring Machine Learning Models to Uncover Pathways in ALS Pathogenesis Using Immunohistochemical Features
Kuruvilla, J. M.; Rifai, O. M.; Longden, J.; Gregory, J. M.; Vallejo, M.. medRxiv (preprint), 2026-08-20
Topic: Brain proteins, microglia and lab models
Hexanucleotide repeat expansions in C9orf72 are the most common genetic cause of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia, yet the predictive information contained within associated inflammatory and proteinopathy markers remains incompletely characterised. Here, we reconstructed five machine-learning-ready datasets from quantitative post-mortem measurements of Iba1, CD68, GFAP, TDP-43 and FUS obtained from 10 C9orf72-ALS cases and 10 controls, building upon the digital pathology study of Rifai et al.. Random forest, support vector machine, XGBoost, logistic regression, artificial neural network and ensemble approaches were evaluated, together with preprocessing, hyperparameter optimisation and biomarker-specific feature importance. Under conventional 3-fold cross-validation, model performance varied according to both biomarker and classifier, with no single algorithm consistently achieving the highest sensitivity, specificity and accuracy across all biomarkers. Iba1 showed the strongest random forest performance, reaching 88% sensitivity and 83% specificity. However, when random forest was evaluated using patient-grouped 5-fold cross-validation, performance decreased across all five biomarkers, with reductions of 6.98-14.52% in accuracy, 5.68-18.90% in sensitivity and 2.86-13.04% in specificity relative to conventional cross-validation. Iba1 nevertheless remained the strongest-performing random forest biomarker under patient-grouped evaluation. Feature-importance analysis further showed biomarker-specific predictive profiles, with classification generally distributed across multiple pathological measurements rather than dominated by a single feature. These findings demonstrate that estimates of machine-learning performance in quantitative neuropathology are sensitive to validation design and highlight the importance of patient-level separation when multiple pathological observations originate from the same individuals. Further evaluation using fully nested patient-grouped cross-validation is required to establish comparative model performance on unseen patients.
Key findings
- These findings demonstrate that estimates of machine-learning performance in quantitative neuropathology are sensitive to validation design and highlight the importance of patient-level separation when multiple pathological observations originate from the same individuals.
Why it matters
Machine-learning models using post-mortem inflammatory and proteinopathy markers in C9orf72-ALS performed worse when validation kept observations from the same person together. This is a methods finding about neuropathology studies of ALS and frontotemporal dementia, not a treatment or cure.
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Study details
- Effect size14.52%; 18.90%; 13.04%
- Evidence strengthLow
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 21 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it. We have no data on impact for its field and year, so that was left out: it earned 20 of the 94 points we could judge it on, which is 21 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (medRxiv (preprint)) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 5 / 5 | 2026 — within 1 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 0 / 7 | Cited 0 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.0 citations a year over 2 year(s) since 2026 |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Marta Vallejo: h-index 10 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Columbia University Irving Medical Center: institution h-index 1,047 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 3 institution(s) in 2 countries |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Points | 20 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 20 of the 94 points we could judge it on is 21 out of 100. |
| Total | 21 / 100 | Capped to 16 because this is not a peer-reviewed article. |
What each of these 20 components measures, and what this score is not →
Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Kuruvilla, J. M., Rifai, O. M., Longden, J., Gregory, J. M., & Vallejo, M. (2026). Exploring Machine Learning Models to Uncover Pathways in ALS Pathogenesis Using Immunohistochemical Features. medRxiv (preprint). https://doi.org/10.64898/2025.12.19.25342356
Vancouver
Kuruvilla JM, Rifai OM, Longden J, Gregory JM, Vallejo M. Exploring Machine Learning Models to Uncover Pathways in ALS Pathogenesis Using Immunohistochemical Features. medRxiv (preprint). 2026. doi:10.64898/2025.12.19.25342356.
BibTeX
@article{kuruvilla2026-cd8b8955,
author = {Kuruvilla, J. M. and Rifai, O. M. and Longden, J. and Gregory, J. M. and Vallejo, M.},
title = {{Exploring Machine Learning Models to Uncover Pathways in ALS Pathogenesis Using Immunohistochemical Features}},
journal = {medRxiv (preprint)},
year = {2026},
doi = {10.64898/2025.12.19.25342356},
url = {https://doi.org/10.64898/2025.12.19.25342356}
}
Studies this one cites
Referenced by this study and also in the alzhub library, so you can read them here.