ArticleScientific reports2025
Protein function prediction using GO similarity-based heterogeneous network propagation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Targeting protein protein interactions and their modulators to enable new therapeutic strategies for human diseases.NPJ systems biology and applications · 2026Review
- Evaluating network-based algorithms for protein function prediction using plant protein-protein interaction networks.BMC plant biology · 2026Article
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
- Vector semantics of multidomain protein architectures.Bioinformatics advances · 2026Article
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Authors and funding
2 authors.
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Abstract
Protein function prediction is a fundamental cornerstone in bioinformatics, providing critical insights into biological processes and disease mechanisms. Despite significant advances, challenges persist due to data sparsity and functional ambiguity. We introduce GOHPro (GO Similarity-based Heterogeneous Network Propagation), a novel method that constructs a heterogeneous network by integrating protein functional similarity (derived from domain profiles and modular complexes) with GO semantic relationships. This method applies a network propagation algorithm to prioritize annotations based on multi-omics context. When evaluated on yeast and human datasets, GOHPro outperformed six state-of-the-art methods. Specifically, it achieved F
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