Topic: Genetics and APOE
We obtained a predictive accuracy of 0.71 (95 % CI [0.59, 0.84]) with an AUC of 0.69 in the HRS test dataset and got an accuracy of 0.62 (95 % CI [0.56, 0.68]) with an AUC of 0.63 in the ROSMAP dataset. This is the first study showing the generalizability of a deep learning prediction model for dementia using genetic variants in an independent cohort. The latent features identified using E-VAE can help us understand the biology of AD/ ADRD and better characterize disease status.
Key findings
- We obtained a predictive accuracy of 0.71 (95 % CI [0.59, 0.84]) with an AUC of 0.69 in the HRS test dataset and got an accuracy of 0.62 (95 % CI [0.56, 0.68]) with an AUC of 0.63 in the ROSMAP dataset.
- This is the first study showing the generalizability of a deep learning prediction model for dementia using genetic variants in an independent cohort.
- The latent features identified using E-VAE can help us understand the biology of AD/ ADRD and better characterize disease status.
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Study details
- Participants234
- Evidence strengthModerate
How this study scored 54/100
Published in a peer-reviewed journal. Scored zero for study design, effect reported and randomised / blinded because the record does not state it. We have no data on journal's citation record, times cited, citations per year, impact for its field and year, cited by studies here, still being cited, senior author's track record, institution's track record, multi-centre collaboration and free to read, so those were left out: it earned 30 of the 56 points we could judge it on, which is 54 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Journal of biomedical informatics) |
| Journal's citation record | — / 4 | No journal record from OpenAlex — left out of this score |
| 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 | 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 | 2 / 4 | 234 participants |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 3 / 3 | Studied in people (participant count stated) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | — / 7 | No citation count (not found in OpenAlex) — left out of this score |
| Citations per year | — / 5 | No citation count or no year — left out of this score |
| 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 | — / 6 | Not yet checked against this library's citation graph — left out of this score |
| Still being cited | — / 4 | No yearly citation counts — left out of this score |
| Senior author's track record | — / 4 | Senior author's record not available — left out of this score |
| Institution's track record | — / 3 | No institution listed in OpenAlex — left out of this score |
| Multi-centre collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 37926392) |
| Free to read | — / 2 | Access not recorded — left out of this score |
| Points | 30 / 56 | Scored on 10 of 20 parts. We have no data for the rest, so they are left out: 30 of the 56 points we could judge it on is 54 out of 100. |
| Total | 54 / 100 | — |
What each of these 20 components measures, and what this score is not →
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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
Vivek, S., Faul, J., Thyagarajan, B., & Guan, W. (2023). Explainable variational autoencoder (E-VAE) model using genome-wide SNPs to predict dementia. Journal of biomedical informatics. https://doi.org/10.1016/j.jbi.2023.104536
Vancouver
Vivek S, Faul J, Thyagarajan B, Guan W. Explainable variational autoencoder (E-VAE) model using genome-wide SNPs to predict dementia. Journal of biomedical informatics. 2023. doi:10.1016/j.jbi.2023.104536. PMID: 37926392.
BibTeX
@article{vivek2023-37926392,
author = {Vivek, Sithara and Faul, Jessica and Thyagarajan, Bharat and Guan, Weihua},
title = {{Explainable variational autoencoder (E-VAE) model using genome-wide SNPs to predict dementia}},
journal = {Journal of biomedical informatics},
year = {2023},
doi = {10.1016/j.jbi.2023.104536},
pmid = {37926392},
url = {https://doi.org/10.1016/j.jbi.2023.104536}
}