UFGOT: Unbalanced Filter Graph Alignment with Optimal Transport for Cancer Subtyping Based on Multi-omics Data
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5128-0_11
Reference19 articles.
1. Kriebel, A.R., Welch, J.D.: UINMF performs mosaic integration of single-cell multi-omic datasets using nonnegative matrix factorization. Nat. Commun. 13, 780 (2022)
2. Argelaguet, R., et al.: MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data. Genome Biol. 21, 111 (2020)
3. Yang, H., Chen, R., Li, D., Wang, Z., Robinson, P.: Subtype-GAN: a deep learning approach for integrative cancer subtyping of multi-omics data. Bioinformatics 37, 2231–2237 (2021)
4. Demetci, P., Santorella, R., Sandstede, B., Noble, W.S., Singh, R.: SCOT: Single-cell Mul-ti-Omics alignment with optimal transport. J. Comput. Biol. 29, 3–18 (2022)
5. Demetci, P., Santorella, R., Chakravarthy, M., Sandstede, B., Singh, R.: SCOTv2: Single-cell Multiomic alignment with disproportionate cell-type representation. J. Comput. Biol. 29, 1213–1228 (2022)
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