@inproceedings{b9547fd7cfba428da3146e8b2ba81e19,
title = "GE-MFAT: Heterogeneous Graph Feature Transfer with Focusing Attention for Protein-Metabolite Interaction Prediction",
abstract = "Protein metabolite interactions (PMIs) play a crucial role in cellular homeostasis, supporting drug development and revealing biological processes. Traditional detection methods are constrained by resource limitations, making large-scale PMI identification challenging. Existing machine learning approaches for PMI prediction often fail to capture correlation features between proteins and metabolites and struggle with crossspecies and batch effect problems. We propose GCN Embedding-Multihead Focusing Attention (GE-MFAT), a novel method that combines heterogeneous graph neural networks with a focusing attention mechanism to extract complex PMI relationships. Our model employs transfer learning to address cross-species and batch effect challenges. Experimental results across three datasets demonstrate that GE-MFAT significantly outperforms state-of-the-art methods across all evaluation metrics.",
keywords = "PMI, focus attention mechanism, heterogeneous graph convolutional network, transfer learning",
author = "Yuzhi Sun and Renjie Liu and Liyuan Zhang and Tianyi Zhao",
note = "Publisher Copyright: {\textcopyright} 2025 IEEE.; 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025 ; Conference date: 15-12-2025 Through 18-12-2025",
year = "2025",
doi = "10.1109/BIBM66473.2025.11356109",
language = "英语",
series = "Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "7719--7726",
editor = "Juan Liu and Jingshan Huang and Xiaowo Wang and Fa Zhang and Xiufen Zou and Tian Tian and Xiaohua Hu and Bin Hu and Yi Xiong",
booktitle = "Proceedings - 2025 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2025",
address = "美国",
}