ArticleCells2019
A High Efficient Biological Language Model for Predicting Protein⁻Protein Interactions.
Article in Cells, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Optimizing protein tokenization: reduced amino acid alphabets for efficient and accurate protein language models.Bioinformatics (Oxford, England) · 2026Article
- Heavy-chain immune repertoire sequencing enables language-model prediction of antigen-specific antibodies.Research square · 2026Article
- Component puzzle protein-protein interaction prediction.Briefings in bioinformatics · 2025Article
- GeoEvoBuilder: A deep learning framework for efficient functional and thermostable protein design.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Hybrid protein-ligand binding residue prediction with protein language models: does the structure matter?Bioinformatics (Oxford, England) · 2025Article
- Generative and Contrastive Self-Supervised Learning for Virulence Factor Identification Based on Protein-Protein Interaction Networks.Microorganisms · 2025Article
- A molecular representation system with a common reference frame for analyzing triterpenoid structural diversity.Plant communications · 2025Article
- Negative sampling strategies impact the prediction of scale-free biomolecular network interactions with machine learning.BMC biology · 2025Article
- Feature fusion with attributed deepwalk for protein-protein interaction prediction.Scientific reports · 2025Article
- Natural Language Processing Methods for the Study of Protein-Ligand Interactions.Journal of chemical information and modeling · 2025Review
- Evaluating data partitioning strategies for accurate prediction of protein-ligand binding free energy changes in mutated proteins.Computational and structural biotechnology journal · 2025Article
- Advances in protein-protein interaction prediction: a deep learning perspective.Frontiers in bioinformatics · 2025Review
- Mutational insights andFrontiers in bioinformatics · 2025Article
- Article
- Generative language models on nucleotide sequences of human genes.Scientific reports · 2024Article
- Advancing plant biology through deep learning-powered natural language processing.Plant cell reports · 2024Review
- Computational tools for plant genomics and breeding.Science China. Life sciences · 2024Review
- Prediction of Protein-Protein Interactions Based on Integrating Deep Learning and Feature Fusion.International journal of molecular sciences · 2024Article
- Protein features fusion using attributed network embedding for predicting protein-protein interaction.BMC genomics · 2024Article
- Analysis and prediction of interactions between transmembrane and non-transmembrane proteins.BMC genomics · 2024Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Many life activities and key functions in organisms are maintained by different types of protein⁻protein interactions (PPIs). In order to accelerate the discovery of PPIs for different species, many computational methods have been developed. Unfortunately, even though computational methods are constantly evolving, efficient methods for predicting PPIs from protein sequence information have not been found for many years due to limiting factors including both methodology and technology. Inspired by the similarity of biological sequences and languages, developing a biological language processing technology may provide a brand new theoretical perspective and feasible method for the study of biological sequences. In this paper, a pure biological language processing model is proposed for predicting protein⁻protein interactions only using a protein sequence. The model was constructed based on a feature representation method for biological sequences called bio-to-vector (Bio2Vec) and a convolution neural network (CNN). The Bio2Vec obtains protein sequence features by using a "bio-word" segmentation system and a word representation model used for learning the distributed representation for each "bio-word". The Bio2Vec supplies a frame that allows researchers to consider the context information and implicit semantic information of a bio sequence. A remarkable improvement in PPIs prediction performance has been observed by using the proposed model compared with state-of-the-art methods. The presentation of this approach marks the start of "bio language processing technology," which could cause a technological revolution and could be applied to improve the quality of predictions in other problems.
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