![[feed]](/style/images/feed-icon-14x14.png){kind=icon}
Tools
Tools
Preview |
PDF (Original Article)
- Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
4MB |
![[thumbnail of Supplementary Information]](https://edoc.mdc-berlin.de/style/images/fileicons/other.png) |
Other (Supplementary Information)
3MB |
| Item Type: | Article |
|---|---|
| Title: | A knowledge graph to interpret clinical proteomics data |
| Creators Name: | Santos, A., Colaço, A.R., Nielsen, A.B., Niu, L., Strauss, M., Geyer, P.E., Coscia, F., Albrechtsen, N.J.W., Mundt, F., Jensen, L.J. and Mann, M. |
| Abstract: | Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple biomedical databases and publications, pose a challenge to data integration. Here we present the Clinical Knowledge Graph (CKG), an open-source platform currently comprising close to 20 million nodes and 220 million relationships that represent relevant experimental data, public databases and literature. The graph structure provides a flexible data model that is easily extendable to new nodes and relationships as new databases become available. The CKG incorporates statistical and machine learning algorithms that accelerate the analysis and interpretation of typical proteomics workflows. Using a set of proof-of-concept biomarker studies, we show how the CKG might augment and enrich proteomics data and help inform clinical decision-making. |
| Keywords: | Algorithms, Machine Learning, Automated Pattern Recognition, Precision Medicine, Proteomics |
| Source: | Nature Biotechnology |
| ISSN: | 1087-0156 |
| Publisher: | Nature Publishing Group |
| Volume: | 40 |
| Number: | 5 |
| Page Range: | 692-702 |
| Date: | 22 May 2022 |
| Official Publication: | https://doi.org/10.1038/s41587-021-01145-6 |
| PubMed: | View item in PubMed |
Repository Staff Only: item control page
