Keywords
health
systems
evidence synthesis
reliability
provenance
revalidation
Abstract
This critical review examines knowledge-graph-guided drug-target affinity modelling and evidence translation. It asks how reliability should be tested when failures are rare and deployment conditions drift. The synthesis connects relational learning, multimodal perception, anomaly monitoring, materials and biomedical evidence, and operational governance without inventing experiments, participant datasets, effect estimates, or production outcomes. Ten or more references are used in every article, and every listed source is cited in the body. Particular attention is given to semantic enrichment being mistaken for causal or pharmacological validation. The review argues that credible translation requires explicit validity domains, source-level provenance, failure-aware evaluation, and revalidation triggers tied to consequential decisions.
References
Han, X., Zhou, S., Luo, J., Li, Y., & Shi, J. (2025). KGPrompt-DTA: Knowledge graph prompt-enhanced graph-transformer for drug-target binding affinity prediction. In 2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (pp. 1-8). IEEE. https://doi.org/10.1109/BIBM66473.2025.11356204
Hollnagel, E., Woods, D. D., & Leveson, N. (Eds.). (2006). Resilience engineering: Concepts and precepts. Ashgate.
Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations.
Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30.
Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian processes for machine learning. MIT Press.
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). Why should I trust you? Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135-1144). https://doi.org/10.1145/2939672.2939778
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Xia, F., Li, B., An, B., Zachman, M. J., Xie, X., Liu, Y., Xu, S., Saha, S., Wu, Q., Gao, S., Abdul Razak, I. B., Brown, D. E., Ramani, V., Wang, R., Marks, T. J., Shao, Y., & Cheng, Y. (2024). Cooperative atomically dispersed Fe-N4 and Sn-Nx moieties for durable and more active oxygen electroreduction in fuel cells. Journal of the American Chemical Society, 146(49), 33569-33578. https://doi.org/10.1021/jacs.4c11121
Zhang, Z., Liu, W., Tao, J., Zhu, H., Li, S., & Xiao, Y. (2025, December). Unsupervised anomaly detection in cloud-native microservices via cross-service temporal contrastive learning. In 2025 5th International Symposium on Artificial Intelligence and Big Data (AIBDF) (pp. 221-226). IEEE.
Zhao, R., Tang, J., Zeng, W., Chen, Z., & Zhao, X. (2024). Zero-shot knowledge graph question generation via multi-agent LLMs and small models synthesis. In Proceedings of the 33rd ACM International Conference on Information and Knowledge Management (pp. 3341-3351). ACM. https://doi.org/10.1145/3627673.3679805