Learning an Adaptive Self-expressive Fusion Model for Multi-omics Cancer Subtype Prediction
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5128-0_22
Reference23 articles.
1. Benkirane, H., Pradat, Y., Michiels, S., Cournède, P.H.: CustOmics: a versatile deep-learning based strategy for multi-omics integration. PLoS Comput. Biol. 19(3), e1010921 (2023)
2. Cai, Y., Wang, S.: Deeply integrating latent consistent representations in high-noise multi-omics data for cancer subtyping. Briefings Bioinf. 25(2), bbae061 (2024)
3. Chen, W., Wang, H., Liang, C.: Deep multi-view contrastive learning for cancer subtype identification. Briefings Bioinf. 24(5), bbad282 (2023)
4. Chen, Y., et al.: MOCSS: multi-omics data clustering and cancer subtyping via shared and specific representation learning. iScience 26(8) (2023)
5. Dickson, G.J., et al.: Hoxa/pbx3 knockdown impairs growth and sensitizes cytogenetically normal acute myeloid leukemia cells to chemotherapy. Haematologica 98(8), 1216 (2013)
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