A Contrastive-Learning-Based Deep Neural Network for Cancer Subtyping by Integrating Multi-Omics Data
Author:
Funder
National Natural Science Foundation of China
Jihua Laboratory
Major key project of PCL
Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s12539-024-00641-y.pdf
Reference39 articles.
1. Wei Y, Li L, Zhao X et al (2023) Cancer subtyping with heterogeneous multi-omics data via hierarchical multi-kernel learning. Brief Bioinform 24(1):bbac488. https://doi.org/10.1093/bib/bbac488
2. Tsai PC, Lee TH, Kuo KC et al (2023) Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients. Nat Commun 14(1):2102. https://doi.org/10.1038/s41467-023-37179-4
3. Dhillon A, Singh A, Bhalla VK (2023) A systematic review on biomarker identification for cancer diagnosis and prognosis in multi-omics: from computational needs to machine learning and deep learning. Arch Comput Methods Eng 30(2):917–949. https://doi.org/10.1007/s11831-022-09821-9
4. Sun D, Li A, Tang B et al (2018) Integrating genomic data and pathological images to effectively predict breast cancer clinical outcome. Comput Methods Programs Biomed 161:45–53. https://doi.org/10.1016/j.cmpb.2018.04.008
5. Haldar D, Kazerooni AF, Arif S et al (2023) Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers. Neoplasia 36:100869. https://doi.org/10.1016/j.neo.2022.100869
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