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MFC-PPI: protein–protein interaction prediction with multimodal feature fusion and contrastive learning

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Abstract

Protein–protein interactions (PPIs) are of critical importance in numerous biological processes and disease mechanisms, and the accurate prediction of PPIs is helpful in the comprehension of complex biological systems. In this paper, MFC-PPI, a PPI prediction model based on multimodal feature fusion and contrastive learning, is proposed. The sequential features, structural features, and PPI network features of proteins are extracted and combined for prediction. The contrastive learning is used to compare the subtle difference between the sequential features and structural features. In addition, the feature enhancement module is designed for feature fusion. The comparative experiments on SHS27k and SHS148k datasets demonstrates the excellent performance of MFC-PPI over other state-of-art methods under three partitioning strategies, Random, BFS, and DFS.

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Data availability

The implementation of MFC-PPI and the preprocessed data is available at: https://github.com/Zhangzxin24/MFC-PPI.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (12371491).

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Authors and Affiliations

  1. College of Computer Science and Technology, Qingdao University, No. 308 Ningxia Road, Qingdao, 266071, Shandong, China

    Zhixin Zhang, Qunhao Zhang, Jun Xiao, Shanyang Ding & Zhen Li

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  1. Zhixin Zhang
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  2. Qunhao Zhang
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  3. Jun Xiao
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  4. Shanyang Ding
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  5. Zhen Li
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Contributions

Z.Z.wrote the main manuscript text. J.X., Q.Z., and S.D. contributed to data analysis and figure preparation. Z.L. supervised the research and reviewed the manuscript. All authors approved the final version of the manuscript.

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Correspondence to Zhen Li.

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Zhang, Z., Zhang, Q., Xiao, J. et al. MFC-PPI: protein–protein interaction prediction with multimodal feature fusion and contrastive learning. J Supercomput 81, 579 (2025). https://doi.org/10.1007/s11227-025-07076-2

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