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Accurate Blood-Based Diagnostic Biosignatures for Alzheimer's Disease via Automated Machine Learning
Karaglani M, Gourlia K, Tsamardinos I, Chatzaki E.. Journal of clinical medicine, 2020-09-18
Topic: Amyloid, tau and blood-test diagnosis
Alzheimer's disease (AD) is the most common form of neurodegenerative dementia and its timely diagnosis remains a major challenge in biomarker discovery. In the present study, we analyzed publicly available high-throughput low-sample -omics datasets from studies in AD blood, by the AutoML technology Just Add Data Bio (JADBIO), to construct accurate predictive models for use as diagnostic biosignatures. Considering data from AD patients and age-sex matched cognitively healthy individuals, we produced three best performing diagnostic biosignatures specific for the presence of AD: A. A 506-feature transcriptomic dataset from 48 AD and 22 controls led to a miRNA-based biosignature via Support Vector Machines with three miRNA predictors (AUC 0.975 (0.906, 1.000)), B. A 38,327-feature transcriptomic dataset from 134 AD and 100 controls led to six mRNA-based statistically equivalent signatures via Classification Random Forests with 25 mRNA predictors (AUC 0.846 (0.778, 0.905)) and C. A 9483-feature proteomic dataset from 25 AD and 37 controls led to a protein-based biosignature via Ridge Logistic Regression with seven protein predictors (AUC 0.921 (0.849, 0.972)). These performance metrics were also validated through the JADBIO pipeline confirming stability. In conclusion, using the automated machine learning tool JADBIO, we produced accurate predictive biosignatures extrapolating available low sample -omics data. These results offer options for minimally invasive blood-based diagnostic tests for AD, awaiting clinical validation based on respective laboratory assays. They also highlight the value of AutoML in biomarker discovery.
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APA 7th
Karaglani, M., Gourlia, K., Tsamardinos, I., & Chatzaki, E. (2020). Accurate Blood-Based Diagnostic Biosignatures for Alzheimer's Disease via Automated Machine Learning. Journal of clinical medicine. https://doi.org/10.3390/jcm9093016
Vancouver
Karaglani M, Gourlia K, Tsamardinos I, Chatzaki E. Accurate Blood-Based Diagnostic Biosignatures for Alzheimer's Disease via Automated Machine Learning. Journal of clinical medicine. 2020. doi:10.3390/jcm9093016. PMID: 32962113.
BibTeX
@article{karaglani2020-32962113,
author = {Karaglani, M. and Gourlia, K. and Tsamardinos, I. and Chatzaki, E.},
title = {{Accurate Blood-Based Diagnostic Biosignatures for Alzheimer's Disease via Automated Machine Learning}},
journal = {Journal of clinical medicine},
year = {2020},
doi = {10.3390/jcm9093016},
pmid = {32962113},
url = {https://doi.org/10.3390/jcm9093016}
}
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
- In conclusion, using the automated machine learning tool JADBIO, we produced accurate predictive biosignatures extrapolating available low sample -omics data.
- These results offer options for minimally invasive blood-based diagnostic tests for AD, awaiting clinical validation based on respective laboratory assays.
Why it matters
Using automated machine learning on publicly available blood-based omics datasets, this study produced three diagnostic biosignatures for Alzheimer's disease with AUCs ranging from 0.846 to 0.975. These results offer options for minimally invasive blood-based diagnostic tests, but the authors note they await clinical validation based on respective laboratory assays.
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Study details
- Effect sizeAUC 0.975; AUC 0.846; AUC 0.921
- Evidence strengthLow
How this study scored 45/100
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 (Journal of clinical medicine) |
| Journal's citation record | 2 / 4 | Journal of Clinical Medicine: journal h-index 188 (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 | 2 / 5 | 2020 — within 10 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 53 times (OpenAlex, 2026-10-10) |
| Citations per year | 2 / 5 | 6.6 citations a year over 8 year(s) since 2020 |
| Impact for its field and year | 3 / 6 | Cited 5.3× 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 | 2 / 4 | Cited 20 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Εkaterini Chatzaki: h-index 34 (OpenAlex, whole career) |
| Institution's track record | 0 / 3 | University of Crete: institution h-index 462 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 5 institution(s) in 1 country |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 32962113) |
| Free to read | 2 / 2 | Free to read |
| Total | 45 / 100 | — |
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Read the original
- DOI: 10.3390/jcm9093016
- PubMed: 32962113
- Original source →
- Free full text: Journal of Clinical Medicine →
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
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