首页|Dual alignment feature embedding network for multi-omics data clustering

Dual alignment feature embedding network for multi-omics data clustering

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  • NETL
  • NSTL
  • Elsevier
© 2024 Elsevier B.V.Multi-omics data clustering, with its capability to utilize the biological information of cross-omics to partition cells into their respective clusters, has attracted considerable attention due to its effectiveness for pathological analysis. Aside from cross-omics discrepancy, existing methods suffer from distribution differences, making it difficult to learn high-quality cross-omics consistent information. To tackle this issue, we propose a novel dual alignment feature embedding network for multi-omics data clustering (DAMIC). Specifically, we first utilize an attention-induced feature fusion mechanism to capture intra-omics specific and inter-omics structural information for more discriminative features. Moreover, we maximize the mutual information between the unified target distribution and other omics-specific assignments by simultaneously optimizing contrastive learning loss and Kullback–Leibler (KL) divergence loss. Finally, we can extract omics-invariant features with robust and rich common embeddings for multi-omics clustering. Extensive experimental results on six real-world benchmark datasets demonstrate that our approach surpasses existing state-of-the-art methods in multi-omics data clustering analysis, which provides effective pathologic analysis way for tumors such as Leukemia and Colorectal Neoplasms. The source code is available at https://github.com/YuangXiao/DAMIC.

Attention-induced feature fusionClusteringContrastive learningMulti-omicsMutual information

Xiao Y.、Zou X.、Tang C.、Yang D.、Li J.、Zhou H.

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School of Computer Science China University of Geosciences

School of Computer Science China University of Geosciences||State Key Laboratory of Public Big Data Guizhou University

Department of Colorectal Surgery Tianjin Union Medical Center

Medical School Tianjin University

Department of Hematology Funing People's Hospital

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2025

10.1016/j.knosys.2024.112774

Knowledge-based systems

Knowledge-based systems

SCI
ISSN：0950-7051
年,卷(期)：2025.309(Jan.30)
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