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For: Chai H, Zhou X, Zhang Z, Rao J, Zhao H, Yang Y. Integrating multi-omics data through deep learning for accurate cancer prognosis prediction. Comput Biol Med 2021;134:104481. [PMID: 33989895 DOI: 10.1016/j.compbiomed.2021.104481] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
Number Citing Articles
1 Wang Q, Zhou Y. FedSPL: federated self-paced learning for privacy-preserving disease diagnosis. Brief Bioinform 2021:bbab498. [PMID: 34874995 DOI: 10.1093/bib/bbab498] [Reference Citation Analysis]
2 Stahlschmidt SR, Ulfenborg B, Synnergren J. Multimodal deep learning for biomedical data fusion: a review. Brief Bioinform 2022:bbab569. [PMID: 35089332 DOI: 10.1093/bib/bbab569] [Reference Citation Analysis]
3 Zhang Z, Chai H, Wang Y, Pan Z, Yang Y. Cancer survival prognosis with Deep Bayesian Perturbation Cox Network. Comput Biol Med 2021;:105012. [PMID: 34785075 DOI: 10.1016/j.compbiomed.2021.105012] [Reference Citation Analysis]
4 Caudai C, Galizia A, Geraci F, Le Pera L, Morea V, Salerno E, Via A, Colombo T. AI applications in functional genomics. Comput Struct Biotechnol J 2021;19:5762-90. [PMID: 34765093 DOI: 10.1016/j.csbj.2021.10.009] [Reference Citation Analysis]
5 Chai H, Xia L, Zhang L, Yang J, Zhang Z, Qian X, Yang Y, Pan W. An Adaptive Transfer-Learning-Based Deep Cox Neural Network for Hepatocellular Carcinoma Prognosis Prediction. Front Oncol 2021;11:692774. [PMID: 34646759 DOI: 10.3389/fonc.2021.692774] [Reference Citation Analysis]
6 Zhang C, Chen Y, Zeng T, Zhang C, Chen L. Deep latent space fusion for adaptive representation of heterogeneous multi-omics data. Brief Bioinform 2022:bbab600. [PMID: 35079777 DOI: 10.1093/bib/bbab600] [Reference Citation Analysis]