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Multi2Fusion: A Multiomics Fusion Framework with Multilevel Information Integration for Cancer Subtype

Shanchen Pang¹, Haoran Bi¹, Shudong Wang¹

¹The College of Computer Science and Technology, China University of Petroleum, Qingdao, Shandong 266580, China.

Journal of Chemical Information and Modeling

|March 13, 2026

Summary

This summary is machine-generated.

Multi2Fusion enhances cancer subtype classification by integrating multiomics data at multiple levels. This novel framework effectively captures complex biological patterns, improving personalized treatment strategies.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Accurate cancer subtype classification is vital for personalized medicine.
High-throughput sequencing generates massive multiomics data for cancer research.
Existing fusion methods have limitations in capturing comprehensive cross-omics information.

Purpose of the Study:

To develop a multiomics fusion framework, Multi2Fusion, for improved cancer subtype classification.
To fully exploit deep dependencies within and across different omics data modalities.
To enhance the integration of complementary information from multiomics data.

Main Methods:

Constructed a sample-gene heterogeneity graph combined with heterogeneous graph convolution.
Implemented a dual-level fusion mechanism integrating feature and label levels.
Employed adaptive fusion of omics features and joint optimization of multiview predictions.

Main Results:

Multi2Fusion demonstrated superior performance across all classification metrics compared to existing methods.
The framework effectively captured structured intraomics features and high-order cross-omics associations.
Validated effectiveness on four public cancer multiomics datasets.

Conclusions:

Multi2Fusion offers a powerful approach for multiomics data integration in cancer research.
The multilevel information integration framework significantly improves cancer subtype classification accuracy.
This method holds promise for advancing personalized cancer treatment strategies.