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Exploiting machine learning models to identify novel Alzheimer's disease biomarkers and potential targets
Alamro H, Thafar MA, Albaradei S, Gojobori T, Essack M, Gao X.. Scientific reports, 2023-03-27
Topic: Amyloid, tau and blood-test diagnosis
We still do not have an effective treatment for Alzheimer's disease (AD) despite it being the most common cause of dementia and impaired cognitive function. Thus, research endeavors are directed toward identifying AD biomarkers and targets. In this regard, we designed a computational method that exploits multiple hub gene ranking methods and feature selection methods with machine learning and deep learning to identify biomarkers and targets. First, we used three AD gene expression datasets to identify 1/ hub genes based on six ranking algorithms (Degree, Maximum Neighborhood Component (MNC), Maximal Clique Centrality (MCC), Betweenness Centrality (BC), Closeness Centrality, and Stress Centrality), 2/ gene subsets based on two feature selection methods (LASSO and Ridge). Then, we developed machine learning and deep learning models to determine the gene subset that best distinguishes AD samples from the healthy controls. This work shows that feature selection methods achieve better prediction performances than the hub gene sets. Beyond this, the five genes identified by both feature selection methods (LASSO and Ridge algorithms) achieved an AUC = 0.979. We further show that 70% of the upregulated hub genes (among the 28 overlapping hub genes) are AD targets based on a literature review and six miRNA (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, hsa-mir-26a-5p, hsa-mir-93-5p, hsa-mir-155-5p) and one transcription factor, JUN, are associated with the upregulated hub genes. Furthermore, since 2020, four of the six microRNA were also shown to be potential AD targets. To our knowledge, this is the first work showing that such a small number of genes can distinguish AD samples from healthy controls with high accuracy and that overlapping upregulated hub genes can narrow the search space for potential novel targets.
Key findings
- We further show that 70% of the upregulated hub genes (among the 28 overlapping hub genes) are AD targets based on a literature review and six miRNA (hsa-mir-16-5p, hsa-mir-34a-5p, hsa-mir-1-3p, hsa-mir-26a-5p, hsa-mir-93-5p, hsa-mir-155-5p) and one transcription factor, JUN, are associated with the upregulated hub genes.
Why it matters
This computational study used gene expression datasets and machine learning to identify genes that distinguish Alzheimer's disease samples from healthy controls, with five genes achieving an AUC of 0.979. It identifies potential biomarkers and targets, but it does not test any treatment, cure or prevention in patients.
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Study details
- Effect sizeAUC = 0.979
- Evidence strengthLow
How this study scored 51/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Scientific reports) |
| Journal's citation record | 3 / 4 | Scientific Reports: journal h-index 463 (OpenAlex, 2026-10-29) |
| 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 | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2023 — within 6 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 | 3 / 3 | Effect reported with a statistic (interval, p-value, ratio or similar) |
| 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 | 4 / 7 | Cited 61 times (OpenAlex, 2026-10-10) |
| Citations per year | 3 / 5 | 12.2 citations a year over 5 year(s) since 2023 |
| Impact for its field and year | 4 / 6 | Cited 8.9× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 1 / 6 | Cited by 2 other studies in this library |
| Still being cited | 3 / 4 | Cited 56 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 3 / 4 | Last-listed author Xin Gao: h-index 74 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | King Abdullah University of Science and Technology: institution h-index 692 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 36973386) |
| Free to read | 2 / 2 | Free to read |
| Total | 51 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
- DOI: 10.1038/s41598-023-30904-5
- PubMed: 36973386
- Original source →
- Free full text: Scientific Reports →
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
Alamro, H., Thafar, M. A., Albaradei, S., Gojobori, T., Essack, M., & Gao, X. (2023). Exploiting machine learning models to identify novel Alzheimer's disease biomarkers and potential targets. Scientific reports. https://doi.org/10.1038/s41598-023-30904-5
Vancouver
Alamro H, Thafar MA, Albaradei S, Gojobori T, Essack M, Gao X. Exploiting machine learning models to identify novel Alzheimer's disease biomarkers and potential targets. Scientific reports. 2023. doi:10.1038/s41598-023-30904-5. PMID: 36973386.
BibTeX
@article{alamro2023-36973386,
author = {Alamro, H. and Thafar, M. A. and Albaradei, S. and Gojobori, T. and Essack, M. and Gao, X.},
title = {{Exploiting machine learning models to identify novel Alzheimer's disease biomarkers and potential targets}},
journal = {Scientific reports},
year = {2023},
doi = {10.1038/s41598-023-30904-5},
pmid = {36973386},
url = {https://doi.org/10.1038/s41598-023-30904-5}
}
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