GraphPI: Efficient Protein Inference with Graph Neural Networks

• URL: http://arxiv.org/abs/2605.04376v1
• Date: Wed, 06 May 2026 00:55:10 GMT
• Title: GraphPI: Efficient Protein Inference with Graph Neural Networks
• Abstract summary: We introduce GraphPI, a novel framework that treats protein inference as a node classification problem.<n>We treat proteins as interconnected nodes within a protein-peptide-PSM graph, utilizing a Graph Neural Network-based architecture to elucidate their interrelations.<n>Our experiments reveal notable performance on various test datasets and deliver significantly reduced computation times compared to common protein inference algorithms.
• Score: 5.080679217338999
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite these strides, its application in protein inference is impeded by the scarcity of extensively labeled datasets, a challenge compounded by the high costs and complexities of accurate protein annotation. In this study, we introduce GraphPI, a novel framework that treats protein inference as a node classification problem. We treat proteins as interconnected nodes within a protein-peptide-PSM graph, utilizing a Graph Neural Network-based architecture to elucidate their interrelations. To address label scarcity, we train the model on a set of unlabeled public protein datasets with pseudo-labels derived from an existing protein inference algorithm, enhanced by self-training to iteratively refine labels based on confidence scores. Contrary to prevalent methodologies necessitating dataset-specific training, our research illustrates that GraphPI, due to the well normalized nature of Percolator features, exhibits universal applicability without dataset-specific fine-tuning, a feature that not only mitigates the risk of overfitting but also enhances computational efficiency. Our empirical experiments reveal notable performance on various test datasets and deliver significantly reduced computation times compared to common protein inference algorithms.

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