Multimodal Recommender Systems for Predicting Anti-Aging Effects of Animal-Derived Bioactive Peptides
Keywords:
multimodal recommender systems; animal-derived bioactive peptides; anti-aging; system architecture; robustness; fairness; governance; sustainabilityAbstract
The prediction of anti-aging effects from animal-derived bioactive peptides is an emerging interdisciplinary problem that combines peptide chemistry, geroscience, nutrition, pharmacology, machine learning, and recommender-system engineering. This paper examines how multimodal recommender systems can support the prioritization and personalization of peptide candidates whose effects may influence aging-related biological processes. Rather than treating recommendation as a narrow ranking task, the paper frames it as a socio-technical infrastructure that integrates heterogeneous evidence from peptide sequences, biochemical assays, omics measurements, imaging, textual literature, clinical records, and consumer or clinical preference signals. The analysis emphasizes system-level design trade-offs involving data fusion, modality imbalance, cold-start conditions, interpretability, robustness, fairness, privacy, deployment, and long-term sustainability. It also considers governance mechanisms such as model documentation, dataset provenance, internal auditing, and policy oversight for health-related recommendations. A central argument is that multimodal recommender systems for anti-aging peptides should not be evaluated only by predictive accuracy, but also by their ability to maintain evidential traceability, avoid overclaiming, protect vulnerable users, and adapt to evolving scientific consensus. The paper concludes that responsible deployment requires modular architecture, transparent uncertainty communication, domain-specific validation, and coordinated oversight across research, clinical, regulatory, and commercial contexts. Such systems may become useful discovery aids, but only if they are designed as accountable scientific instruments rather than as opaque optimization engines.
References
1. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
2. 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, 5998–6008.
3. Baltrušaitis, T., Ahuja, C., & Morency, L.-P. (2019). Multimodal machine learning: A survey and taxonomy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 423–443.
4. Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning based recommender system: A survey and new perspectives. ACM Computing Surveys, 52(1), Article 5. https://doi.org/10.1145/3285029
5. Covington, P., Adams, J., & Sargin, E. (2016). Deep neural networks for YouTube recommendations. In Proceedings of the 10th ACM Conference on Recommender Systems (pp. 191–198). ACM. https://doi.org/10.1145/2959100.2959190
6. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T.-S. (2017). Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web (pp. 173–182). International World Wide Web Conferences Steering Committee. https://doi.org/10.1145/3038912.3052569
7. Rendle, S., Freudenthaler, C., Gantner, Z., & Schmidt-Thieme, L. (2012). BPR: Bayesian personalized ranking from implicit feedback. arXiv. https://doi.org/10.48550/arXiv.1205.2618
8. Kipf, T. N., & Welling, M. (2017). Semi-supervised classification with graph convolutional networks. In International Conference on Learning Representations.
9. Wang, X., He, X., Wang, M., Feng, F., & Chua, T.-S. (2019). Neural graph collaborative filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (pp. 165–174). ACM. https://doi.org/10.1145/3331184.3331267
10. Liu, T., Song, X., Dai, X., Ma, H., Muhammad, A., He, Z., & Zhao, K. (2025). Effects of animal-derived peptides on aging-related diseases: a narrative review. Aging Advances, 2(4), 160-165.
11. Kingma, D. P., & Ba, J. (2015). Adam: A method for stochastic optimization. In International Conference on Learning Representations.
12. Sculley, D., Holt, G., Golovin, D., Davydov, E., Phillips, T., Ebner, D., Chaudhary, V., Young, M., Crespo, J.-F., & Dennison, D. (2015). Hidden technical debt in machine learning systems. Advances in Neural Information Processing Systems, 28, 2503–2511.
13. Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019). Model cards for model reporting. In Proceedings of the Conference on Fairness, Accountability, and Transparency (pp. 220–229). ACM. https://doi.org/10.1145/3287560.3287596
14. Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé III, H., & Crawford, K. (2021). Datasheets for datasets. Communications of the ACM, 64(12), 86–92. https://doi.org/10.1145/3458723
15. Raji, I. D., Smart, A., White, R. N., Mitchell, M., Gebru, T., Hutchinson, B., Smith-Loud, J., Theron, D., & Barnes, P. (2020). Closing the AI accountability gap: Defining an end-to-end framework for internal algorithmic auditing. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 33–44). ACM. https://doi.org/10.1145/3351095.3372873
16. Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., Luetge, C., Madelin, R., Pagallo, U., Rossi, F., Schafer, B., Valcke, P., & Vayena, E. (2018). AI4People—An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Minds and Machines, 28(4), 689–707. https://doi.org/10.1007/s11023-018-9482-5
17. Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
18. López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M., & Kroemer, G. (2013). The hallmarks of aging. Cell, 153(6), 1194–1217. https://doi.org/10.1016/j.cell.2013.05.039
19. Fontana, L., Partridge, L., & Longo, V. D. (2010). Extending healthy life span—from yeast to humans. Science, 328(5976), 321–326. https://doi.org/10.1126/science.1172539
20. Choi, E., Bahadori, M. T., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Doctor AI: Predicting clinical events via recurrent neural networks. In Proceedings of the 1st Machine Learning for Healthcare Conference (pp. 301–318). PMLR.
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