Interpretable graph-based models on multimodal biomedical data integration: a technical review and benchmarking.
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| Title: | Interpretable graph-based models on multimodal biomedical data integration: a technical review and benchmarking. |
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| Authors: | Sadeghi A; Holcombe Department of Electrical and Computer Engineering, Clemson University, Clemson, SC, USA., Hajati F; School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW, Australia., Argha A; School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, Australia.; Tyree Foundation Institute of Health Engineering (IHealthE), UNSW Sydney, Sydney, NSW, Australia., H Lovell N; School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, Australia.; Tyree Foundation Institute of Health Engineering (IHealthE), UNSW Sydney, Sydney, NSW, Australia., Yang M; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China., Alinejad-Rokny H; UNSW BioMedical Machine Learning Lab (BML), School of Biomedical Engineering, UNSW Sydney, Sydney, NSW, Australia. h.alinejad@unsw.edu.au. |
| Source: | Nature communications [Nat Commun] 2026 Jun 16; Vol. 17 (1). Date of Electronic Publication: 2026 Jun 16. |
| Publication Type: | Journal Article; Review |
| Journal Info: | Publisher: Nature Pub. Group Country of Publication: England NLM ID: 101528555 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1723 (Electronic) Linking ISSN: 20411723 NLM ISO Abbreviation: Nat Commun Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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