Explainable AI for Discovering Disease Biomarkers: A Survey
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-64495-5_14
Reference11 articles.
1. Aronson, J.K., Ferner, R.E.: Biomarkers—a general review. Current Prot. Pharmacol. 76(1) (2017). https://doi.org/10.1002/cpph.19
2. Chakraborty, D., Gutierrez-Chakraborty, E., Rodriguez-Aguayo, C., Başağaoğlu, H., Lopez-Berestein, G., Amero, P.: Discovering genetic biomarkers for targeted cancer therapeutics with eXplainable AI (2023). https://doi.org/10.1101/2023.07.24.550346
3. Fiscal, L.L., Jennebauffe, C., Bruyneel, M., Ris, L., Lefebvre, L., Siebert, X., Gosselin, B.: Explainable ai for EEG biomarkers identification in obstructive sleep apnea severity scoring task. In: 2023 11th International IEEE/EMBS Conference on Neural Engineering (NER). pp. 1–6 (2023). https://doi.org/10.1109/NER52421.2023.10123795
4. Gimeno, M., José-Enériz, E.S., Villar, S., Agirre, X., Prosper, F., Rubio, A., Carazo, F.: Explainable artificial intelligence for precision medicine in acute myeloid leukemia. Front. Immunol. 13 (2022). https://doi.org/10.3389/fimmu.2022.977358
5. Hasan, M.E., Mostafa, F., Hossain, M.S., Loftin, J.: Machine-learning classification models to predict liver cancer with explainable AI to discover associated genes. AppliedMath 3(2), 417–445 (2023). https://doi.org/10.3390/appliedmath3020022
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