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40/100

✓ Peer-reviewed alzhub usefulness score 40 out of 100. Drug development

Topic: Genetics and APOE

Alzheimer's Disease (AD) is a multifactorial disease that shares common etiologies with its multiple comorbidities, especially vascular diseases. To predict repurposable drugs for AD utilizing the relatively well-investigated comorbidities' knowledge, we proposed a multi-task graph neural network (GNN)-based pipeline that incorporates the corresponding biomedical interactome of these diseases with their genetic markers and effective therapeutics. Our pipeline can accurately capture the interactions and disease classification in the network. Next, we predicted drugs that might interact with the AD module by the node embedding similarity. Our candidates are mostly BBB permeable, and literature evidence showed their potential for treating AD pathologies, accompanying symptoms, or cotreating AD pathology and its common comorbidities. Our pipeline demonstrated a workable strategy that predicts drug candidates with current knowledge of biological interplays between AD and several vascular diseases.

Key findings

Our pipeline demonstrated a workable strategy that predicts drug candidates with current knowledge of biological interplays between AD and several vascular diseases.

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Study details

Evidence strengthLow

How this study scored 40/100

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. 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 and multi-centre collaboration, so those were left out: it earned 23 of the 58 points we could judge it on, which is 40 out of 100.

Component	Points	Why
Journal tier	7 / 10	Tier 3 — peer-reviewed journal (AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science)
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	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	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	— / 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 37350918)
Free to read	0 / 2	Behind a paywall — scores zero
Points	23 / 58	Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 23 of the 58 points we could judge it on is 40 out of 100.
Total	40 / 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

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APA 7th

Lin, K. H., Hsieh, K. L., Jiang, X., & Kim, Y. (2023). Integrating Comorbidity Knowledge for Alzheimer's Disease Drug Repurposing using Multi-task Graph Neural Network. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. https://doi.org/10.1101/2020.04.30.069690

Vancouver

Lin KH, Hsieh KL, Jiang X, Kim Y. Integrating Comorbidity Knowledge for Alzheimer's Disease Drug Repurposing using Multi-task Graph Neural Network. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science. 2023. doi:10.1101/2020.04.30.069690. PMID: 37350918.

BibTeX

@article{lin2023-37350918,
  author  = {Lin, K. H. and Hsieh, K. L. and Jiang, X. and Kim, Y.},
  title   = {{Integrating Comorbidity Knowledge for Alzheimer's Disease Drug Repurposing using Multi-task Graph Neural Network}},
  journal = {AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science},
  year    = {2023},
  doi     = {10.1101/2020.04.30.069690},
  pmid    = {37350918},
  url     = {https://doi.org/10.1101/2020.04.30.069690}
}

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