Home › Research › Drug development
Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers
Bang D, Lim S, Lee S, Kim S.. Nature communications, 2023-06-15
Topic: Amyloid, tau and blood-test diagnosis
Plain-language summary
Computational drug repurposing aims to identify new indications for existing drugs by utilizing high-throughput data, often in the form of biomedical knowledge graphs. However, learning on biomedical knowledge graphs can be challenging due to the dominance of genes and a small number of drug and disease entities, resulting in less effective representations. To overcome this challenge, we propose a "semantic multi-layer guilt-by-association" approach that leverages the principle of guilt-by-association - "similar genes share similar functions", at the drug-gene-disease level. Using this approach, our model DREAMwalk: Drug Repurposing through Exploring Associations using Multi-layer random walk uses our semantic information-guided random walk to generate drug and disease-populated node sequences, allowing for effective mapping of both drugs and diseases in a unified embedding space. Compared to state-of-the-art link prediction models, our approach improves drug-disease association prediction accuracy by up to 16.8%. Moreover, exploration of the embedding space reveals a well-aligned harmony between biological and semantic contexts. We demonstrate the effectiveness of our approach through repurposing case studies for breast carcinoma and Alzheimer's disease, highlighting the potential of multi-layer guilt-by-association perspective for drug repurposing on biomedical knowledge graphs.
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
APA 7th
Bang, D., Lim, S., Lee, S., & Kim, S. (2023). Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers. Nature communications. https://doi.org/10.1038/s41467-023-39301-y
Vancouver
Bang D, Lim S, Lee S, Kim S. Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers. Nature communications. 2023. doi:10.1038/s41467-023-39301-y. PMID: 37322032.
BibTeX
@article{bang2023-37322032,
author = {Bang, D. and Lim, S. and Lee, S. and Kim, S.},
title = {{Biomedical knowledge graph learning for drug repurposing by extending guilt-by-association to multiple layers}},
journal = {Nature communications},
year = {2023},
doi = {10.1038/s41467-023-39301-y},
pmid = {37322032},
url = {https://doi.org/10.1038/s41467-023-39301-y}
}
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
- Compared to state-of-the-art link prediction models, our approach improves drug-disease association prediction accuracy by up to 16.8%.
New to this subject? Start with our plain-English guide: Treatments →
Study details
- Effect size16.8%
- Evidence strengthLow
How this study scored 50/100
Published in a leading specialty journal. Scored zero for study design, sample size, studied in people and randomized / 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 multicenter collaboration, so those were left out: it earned 29 of the 58 points we could judge it on, which is 50 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 9 / 10 | Tier 2 — leading specialty journal (Nature communications) |
| 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 | 2 / 3 | Effect reported as a number, without an interval or test |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomized / blinded | 0 / 2 | No randomization 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 |
| Multicenter collaboration | — / 3 | Institutions not listed — left out of this score |
| Indexed in PubMed | 2 / 2 | Indexed in PubMed (PMID 37322032) |
| Free to read | 2 / 2 | Free to read |
| Points | 29 / 58 | Scored on 11 of 20 parts. We have no data for the rest, so they are left out: 29 of the 58 points we could judge it on is 50 out of 100. |
| Total | 50 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
Free copy unknown: no free-copy check has an answer for this record yet. That does not mean it is paywalled.
Related
Studies in the library that share something with this one. The line above each title says what.
Related studies in Drug development from nearby years
Also tagged “multiple”
4 studies in the library carry the topic tag “multiple”; these are this study's neighbours in a fixed, alphabetical-by-hash tour of them, not the closest in content. All 4 tagged studies.