ArticleInternational journal of molecular sciences2026
Hierarchical Contrastive Learning for Protein-Protein Interaction Prediction Across Organisms.
Article in International journal of molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Protein language models are accidental taxonomists.BMC bioinformatics · 2026Article
- Protein Language Models are Accidental Taxonomists.bioRxiv : the preprint server for biology · 2025Article
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Authors and funding
5 authors.
Funding
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Abstract
With advances in biomedical technologies and the continued expansion of experimental resources, biological data are growing rapidly in both scale and complexity. Contrastive learning provides an effective framework for integrating heterogeneous biological information. However, many protein-protein interaction (PPI) prediction methods still represent protein sequences and annotations as flat features and do not explicitly model hierarchical biological relationships among protein families, clans, and functional annotations. Here, we introduce
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.