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SEGT-GO: a graph transformer method based on PPI serialization and explanatory artificial intelligence for protein

Yansong Wang1, Yundong Sun1,2, Baohui Lin3

  • 1School of Computer Science and Technology, Harbin Institute of Technology Weihai Campus, Weihai, 264209, China.

BMC Bioinformatics
|February 10, 2025
PubMed
Summary

SEGT-GO enhances protein function prediction by serializing multi-hop protein-protein interaction (PPI) network information and using explainable AI. This Graph Transformer method improves accuracy and generalization across species.

Keywords:
Explainable artificial intelligenceGraph transformerMulti-hop neighborhood serializationPPI networksProtein function prediction

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Protein function prediction is crucial but challenging due to vast sequence data.
  • Protein-Protein Interaction (PPI) networks are vital for uncovering functional relationships.
  • Current methods struggle with distant protein relationships in PPI networks, with diminishing gains from deeper graph networks.

Purpose of the Study:

  • To develop a novel method for predicting protein function by effectively capturing distant functional relationships in PPI networks.
  • To improve the accuracy and generalization of protein function prediction, especially for large-scale, multi-species datasets.

Main Methods:

  • Proposed SEGT-GO, a Graph Transformer model utilizing multi-hop neighborhood serialization and Explainable AI (XAI).
  • Multi-hop neighborhood serialization converts PPI network information into feature embeddings for deeper learning.
  • SHAP XAI framework optimizes model input and reduces feature noise.

Main Results:

  • SEGT-GO demonstrated competitive performance against DeepGraphGO on large datasets and superior results on small datasets.
  • The method effectively extracts functional information from proteins at deeper network levels.
  • Achieved superior results in cross-species learning and predicting functions of unseen proteins.

Conclusions:

  • SEGT-GO shows strong generalization capabilities for protein function prediction.
  • The approach effectively addresses the challenge of discerning functions from complex protein sequence data.
  • Highlights the potential of combining graph transformers, serialization, and XAI for advanced bioinformatics tasks.