Efficient multi-omics clustering with bipartite graph subspace learning for cancer subtype prediction
Due to the complex nature and highly heterogeneous of cancer, as well as different pathogenesis and clinical features among different cancer subtypes, it was crucial to identify cancer subtypes in cancer diagnosis, prognosis, and treatment. The rapid developments of high-throughput technologies have...
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| Format: | Article |
| Language: | English |
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AIMS Press
2024-11-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2024279 |
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| author | Shuwei Zhu Hao Liu Meiji Cui |
| author_facet | Shuwei Zhu Hao Liu Meiji Cui |
| author_sort | Shuwei Zhu |
| collection | DOAJ |
| description | Due to the complex nature and highly heterogeneous of cancer, as well as different pathogenesis and clinical features among different cancer subtypes, it was crucial to identify cancer subtypes in cancer diagnosis, prognosis, and treatment. The rapid developments of high-throughput technologies have dramatically improved the efficiency of collecting data from various types of omics. Also, integrating multi-omics data related to cancer occurrence and progression can lead to a better understanding of cancer pathogenesis, subtype prediction, and personalized treatment options. Therefore, we proposed an efficient multi-omics bipartite graph subspace learning anchor-based clustering (MBSLC) method to identify cancer subtypes. In contrast, the bipartite graph intended to learn cluster-friendly representations. Experiments showed that the proposed MBSLC method can capture the latent spaces of multi-omics data effectively and showed superiority over other state-of-the-art methods for cancer subtype analysis. Moreover, the survival and clinical analyses further demonstrated the effectiveness of MBSLC. The code and datasets of this paper can be found in https://github.com/Julius666/MBSLC. |
| format | Article |
| id | doaj-art-dc673fa249f14489b3294beb3a0bdd4d |
| institution | Kabale University |
| issn | 2688-1594 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj-art-dc673fa249f14489b3294beb3a0bdd4d2025-01-23T07:53:00ZengAIMS PressElectronic Research Archive2688-15942024-11-0132116008603110.3934/era.2024279Efficient multi-omics clustering with bipartite graph subspace learning for cancer subtype predictionShuwei Zhu0Hao Liu1Meiji Cui2Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi 214122, ChinaJiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence, Jiangnan University, Wuxi 214122, ChinaSchool of Intelligent Manufacturing, Nanjing University of Science and Technology, Nanjing 210094, ChinaDue to the complex nature and highly heterogeneous of cancer, as well as different pathogenesis and clinical features among different cancer subtypes, it was crucial to identify cancer subtypes in cancer diagnosis, prognosis, and treatment. The rapid developments of high-throughput technologies have dramatically improved the efficiency of collecting data from various types of omics. Also, integrating multi-omics data related to cancer occurrence and progression can lead to a better understanding of cancer pathogenesis, subtype prediction, and personalized treatment options. Therefore, we proposed an efficient multi-omics bipartite graph subspace learning anchor-based clustering (MBSLC) method to identify cancer subtypes. In contrast, the bipartite graph intended to learn cluster-friendly representations. Experiments showed that the proposed MBSLC method can capture the latent spaces of multi-omics data effectively and showed superiority over other state-of-the-art methods for cancer subtype analysis. Moreover, the survival and clinical analyses further demonstrated the effectiveness of MBSLC. The code and datasets of this paper can be found in https://github.com/Julius666/MBSLC.https://www.aimspress.com/article/doi/10.3934/era.2024279cancer subtypesmulti-omics databipartite graphlatent spacesspectral clustering |
| spellingShingle | Shuwei Zhu Hao Liu Meiji Cui Efficient multi-omics clustering with bipartite graph subspace learning for cancer subtype prediction Electronic Research Archive cancer subtypes multi-omics data bipartite graph latent spaces spectral clustering |
| title | Efficient multi-omics clustering with bipartite graph subspace learning for cancer subtype prediction |
| title_full | Efficient multi-omics clustering with bipartite graph subspace learning for cancer subtype prediction |
| title_fullStr | Efficient multi-omics clustering with bipartite graph subspace learning for cancer subtype prediction |
| title_full_unstemmed | Efficient multi-omics clustering with bipartite graph subspace learning for cancer subtype prediction |
| title_short | Efficient multi-omics clustering with bipartite graph subspace learning for cancer subtype prediction |
| title_sort | efficient multi omics clustering with bipartite graph subspace learning for cancer subtype prediction |
| topic | cancer subtypes multi-omics data bipartite graph latent spaces spectral clustering |
| url | https://www.aimspress.com/article/doi/10.3934/era.2024279 |
| work_keys_str_mv | AT shuweizhu efficientmultiomicsclusteringwithbipartitegraphsubspacelearningforcancersubtypeprediction AT haoliu efficientmultiomicsclusteringwithbipartitegraphsubspacelearningforcancersubtypeprediction AT meijicui efficientmultiomicsclusteringwithbipartitegraphsubspacelearningforcancersubtypeprediction |