CIGMA: Causal-inspired invariant graph matching with multi-view contrastive distillation for predicting herb-symptom associations.
- DOI
- 10.1016/j.artmed.2026.103509
- Published
- 2026 Nov
- Container
- Artificial intelligence in medicine
- Publisher
- Not recorded
- Open access
- unknown
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limited evidence Score 43/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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Cite this work
BibTeX
@article{allodium:10.1016/j.artmed.2026.103509,
title = {CIGMA: Causal-inspired invariant graph matching with multi-view contrastive distillation for predicting herb-symptom associations.},
author = {Long Q and Zhao N and Liu H and Zhang Q and Yang J},
year = {2026},
journal = {Artificial intelligence in medicine},
doi = {10.1016/j.artmed.2026.103509},
url = {https://doi.org/10.1016/j.artmed.2026.103509}
}RIS
TY - JOUR TI - CIGMA: Causal-inspired invariant graph matching with multi-view contrastive distillation for predicting herb-symptom associations. AU - Long Q AU - Zhao N AU - Liu H AU - Zhang Q AU - Yang J PY - 2026 JO - Artificial intelligence in medicine DO - 10.1016/j.artmed.2026.103509 UR - https://doi.org/10.1016/j.artmed.2026.103509 ER -
APA
Q, L., N, Z., H, L., Q, Z., & J, Y. (2026). CIGMA: Causal-inspired invariant graph matching with multi-view contrastive distillation for predicting herb-symptom associations.. Artificial intelligence in medicine. https://doi.org/10.1016/j.artmed.2026.103509
Source records
- pubmed · retrieved 2026-09-26T05:51:10.310Z