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Multimodal Transformer Modeling of Rapamycin Treatment in Alzheimer's Disease via Random Forest Feature Filtering
Wang, C.; Woods, C.; Nguyen, T.; Liu, J.; Lin, A.-L.; Cheng, J.. medRxiv (preprint), 2026-08-26
Topic: Amyloid, tau and blood-test diagnosis
Plain-language summary
Rapamycin, an FDA-approved inhibitor of the mammalian target of rapamycin (mTOR) pathway, has demonstrated promising anti-aging and neuroprotective effects. Post hoc SHAP-based feature analysis further identifies key biomarkers associated with treatment response, particularly within blood-based and inflammatory modalities. These findings demonstrate that combining feature selection with multimodal representation learning provides a promising and robust approach for modeling treatment effects in small-sample biomedical studies. Importantly, this framework may have significant implications for clinical research and medical applications by identifying the biological features and quantitative measurements that drive individual responses to rapamycin.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Wang, C., Woods, C., Nguyen, T., Liu, J., Lin, A.-L., & Cheng, J. (2026). Multimodal Transformer Modeling of Rapamycin Treatment in Alzheimer's Disease via Random Forest Feature Filtering. medRxiv (preprint). https://doi.org/10.64898/2026.08.22.26361114
Vancouver
Wang C, Woods C, Nguyen T, Liu J, Lin A.-L, Cheng J. Multimodal Transformer Modeling of Rapamycin Treatment in Alzheimer's Disease via Random Forest Feature Filtering. medRxiv (preprint). 2026. doi:10.64898/2026.08.22.26361114.
BibTeX
@article{wang2026-de659fd9,
author = {Wang, C. and Woods, C. and Nguyen, T. and Liu, J. and Lin, A.-L. and Cheng, J.},
title = {{Multimodal Transformer Modeling of Rapamycin Treatment in Alzheimer's Disease via Random Forest Feature Filtering}},
journal = {medRxiv (preprint)},
year = {2026},
doi = {10.64898/2026.08.22.26361114},
url = {https://doi.org/10.64898/2026.08.22.26361114}
}
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.
Key findings
- Rapamycin, an FDA-approved inhibitor of the mammalian target of rapamycin (mTOR) pathway, has demonstrated promising anti-aging and neuroprotective effects.
- Post hoc SHAP-based feature analysis further identifies key biomarkers associated with treatment response, particularly within blood-based and inflammatory modalities.
- These findings demonstrate that combining feature selection with multimodal representation learning provides a promising and robust approach for modeling treatment effects in small-sample biomedical studies.
- Importantly, this framework may have significant implications for clinical research and medical applications by identifying the biological features and quantitative measurements that drive individual responses to rapamycin.
Why it matters
This modelling study applied machine learning to rapamycin treatment data and identified biomarkers associated with treatment response, particularly in blood-based and inflammatory measures. It is a computational framework for small-sample studies and does not itself show that rapamycin treats or prevents Alzheimer's disease.
New to this subject? Start with our plain-English guide: Alzheimer's in numbers →
Study details
- Participants23
- Effect size71.25%
- Evidence strengthModerate
How this study scored 16/100
Preprint — not yet peer-reviewed. It scored 29 on the same rubric, but non-peer-reviewed sources are capped at 16, so it shows as 16. Scored zero for study design and randomized / 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 27 of the 94 points we could judge it on, which is 29 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 | 3 / 5 | Rated “moderate” 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 | 1 / 4 | 23 participants |
| Effect reported | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomized / blinded | 0 / 2 | No randomization 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 | 3 / 4 | Last-listed author Jianlin Jack Cheng: h-index 68 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | University of Missouri: institution h-index 780 (OpenAlex) |
| Multicenter collaboration | 0 / 3 | OpenAlex lists 1 institution(s) in 1 country — scores zero (affiliation lists can be incomplete) |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 2 / 2 | Free to read |
| Points | 27 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 27 of the 94 points we could judge it on is 29 out of 100. |
| Total | 29 / 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).
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