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Multi-omics clustering based on dual contrastive learning for cancer subtype identification

Published: 09 December 2022 Publication History

Abstract

Multi-omics clustering aims to supplement a single omics with data information from multiple omics, assigning samples into their respective clusters without supervision. The existing multi-omics clustering methods tend to use the similarity measure function to construct the similarity network between samples, and then fuse the various omics networks together for clustering by some fusion methods. These methods relies heavily on the original features of the data and the similarity measure function. This paper proposes a novel multi-omics clustering method. The proposed model first uses neural networks to extract the feature embeddings of omics, and then aligns the feature embeddings of different omics in subspaces through contrastive learning. Finally, the feature embeddings are mapped to clustered soft labels, and the soft labels are aligned again using contrastive learning. Experiments show that our method outperforms the baseline methods.

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    ISAIMS '22: Proceedings of the 3rd International Symposium on Artificial Intelligence for Medicine Sciences
    October 2022
    594 pages
    ISBN:9781450398442
    DOI:10.1145/3570773
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Published: 09 December 2022

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    Author Tags

    1. Clustering
    2. Contrastive learning
    3. Multi-omics
    4. Neural networks

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