Topic: Amyloid, tau and blood-test diagnosis
The predALZ framework achieved 94% accuracy on an independent test set and approximately 96% accuracy in cross-validation for Alzheimer-related driver gene prediction. The ensemble model also yielded consistently high sensitivity, specificity, and Matthews correlation coefficient values, indicating stable and reliable classification performance. The model's effectiveness was further rigorously validated through a comprehensive evaluation, using metrics such as accuracy, sensitivity, specificity, and Matthew's correlation coefficient. The study underscores the predictor's remarkable performance, consistently achieving 94% accuracy in an independent Test and ~96% in cross-validation. These findings highlight predALZ's potential for application in predictive diagnostics and targeted therapeutic development for Alzheimer's disease.
Key findings
- The predALZ framework achieved 94% accuracy on an independent test set and approximately 96% accuracy in cross-validation for Alzheimer-related driver gene prediction.
- The ensemble model also yielded consistently high sensitivity, specificity, and Matthews correlation coefficient values, indicating stable and reliable classification performance.
- The model's effectiveness was further rigorously validated through a comprehensive evaluation, using metrics such as accuracy, sensitivity, specificity, and Matthew's correlation coefficient.
- The study underscores the predictor's remarkable performance, consistently achieving 94% accuracy in an independent Test and ~96% in cross-validation.
- These findings highlight predALZ's potential for application in predictive diagnostics and targeted therapeutic development for Alzheimer's disease.
Why it matters
This study describes a machine learning framework that achieved high accuracy in predicting Alzheimer-related driver genes. This is a computational tool for research and does not directly provide a treatment, cure, or prevention for Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 29/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, 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 (Current Drug Targets) |
| Journal's citation record | 1 / 4 | Current Drug Targets: journal h-index 139 (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 | 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 | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| 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 | 0 / 6 | Cited 0.0× the average for papers of its field and year (OpenAlex FWCI) |
| 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 | 2 / 4 | Last-listed author Yaser Daanial Khan: h-index 41 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | Qassim University: institution h-index 207 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 2 countries |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 42163597) |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 29 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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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
Karim, A., Alturise, F., Alkhalifah, T., & Khan, Y. D. (2026). predALZ: An Ensemble Learning Framework for Identifying Genetic Biomarkers in Familial Alzheimer’s Disease. Current Drug Targets. https://doi.org/10.2174/0113894501408301251214192633
Vancouver
Karim A, Alturise F, Alkhalifah T, Khan YD. predALZ: An Ensemble Learning Framework for Identifying Genetic Biomarkers in Familial Alzheimer’s Disease. Current Drug Targets. 2026. doi:10.2174/0113894501408301251214192633. PMID: 42163597.
BibTeX
@article{karim2026-42163597,
author = {Karim, Ayesha and Alturise, Fahad and Alkhalifah, Tamim and Khan, Yaser Daanial},
title = {{predALZ: An Ensemble Learning Framework for Identifying Genetic Biomarkers in Familial Alzheimer’s Disease}},
journal = {Current Drug Targets},
year = {2026},
doi = {10.2174/0113894501408301251214192633},
pmid = {42163597},
url = {https://doi.org/10.2174/0113894501408301251214192633}
}