BPG is committed to discovery and dissemination of knowledge
Cited by in F6Publishing
For: Qiu YL, Zheng H, Devos A, Selby H, Gevaert O. A meta-learning approach for genomic survival analysis. Nat Commun 2020;11:6350. [PMID: 33311484 DOI: 10.1038/s41467-020-20167-3] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
Number Citing Articles
1 Wang R, Chaudhari P, Davatzikos C. Embracing the disharmony in medical imaging: A Simple and effective framework for domain adaptation. Med Image Anal 2021;76:102309. [PMID: 34871931 DOI: 10.1016/j.media.2021.102309] [Reference Citation Analysis]
2 Gevaert O. Meta-learning reduces the amount of data needed to build AI models in oncology. Br J Cancer 2021;125:309-10. [PMID: 33782563 DOI: 10.1038/s41416-021-01358-1] [Cited by in Crossref: 1] [Article Influence: 1.0] [Reference Citation Analysis]
3 Cirillo D, Núñez-Carpintero I, Valencia A. Artificial intelligence in cancer research: learning at different levels of data granularity. Mol Oncol 2021;15:817-29. [PMID: 33533192 DOI: 10.1002/1878-0261.12920] [Cited by in Crossref: 2] [Cited by in F6Publishing: 1] [Article Influence: 2.0] [Reference Citation Analysis]
4 Lecca P. Machine Learning for Causal Inference in Biological Networks: Perspectives of This Challenge. Front Bioinform 2021;1:746712. [DOI: 10.3389/fbinf.2021.746712] [Reference Citation Analysis]
5 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]
6 Del Giudice M, Peirone S, Perrone S, Priante F, Varese F, Tirtei E, Fagioli F, Cereda M. Artificial Intelligence in Bulk and Single-Cell RNA-Sequencing Data to Foster Precision Oncology. Int J Mol Sci 2021;22:4563. [PMID: 33925407 DOI: 10.3390/ijms22094563] [Reference Citation Analysis]
7 Chen H, Lundberg SM, Erion G, Kim JH, Lee SI. Forecasting adverse surgical events using self-supervised transfer learning for physiological signals. NPJ Digit Med 2021;4:167. [PMID: 34880410 DOI: 10.1038/s41746-021-00536-y] [Reference Citation Analysis]
8 Park Y, Hauschild AC, Heider D. Transfer learning compensates limited data, batch effects and technological heterogeneity in single-cell sequencing. NAR Genom Bioinform 2021;3:lqab104. [PMID: 34805988 DOI: 10.1093/nargab/lqab104] [Reference Citation Analysis]
9 Kann BH, Hosny A, Aerts HJWL. Artificial intelligence for clinical oncology. Cancer Cell 2021;39:916-27. [PMID: 33930310 DOI: 10.1016/j.ccell.2021.04.002] [Cited by in Crossref: 3] [Article Influence: 3.0] [Reference Citation Analysis]