ArticleFrontiers in bioengineering and biotechnology2022
DWPPI: A Deep Learning Approach for Predicting Protein-Protein Interactions in Plants Based on Multi-Source Information With a Large-Scale Biological Network.
Article in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- From classical machine learning to emerging foundation models: review on multimodal data integration for cancer research.Artificial intelligence review · 2026Article
- Status and advancement of root-knot nematode management strategies and the emerging CRISPR/Cas biotechnology application.Functional & integrative genomics · 2026Review
- Capturing protein-protein interactions in plants: recent advances, challenges, and opportunities.Frontiers in molecular biosciences · 2026Review
- Recent Advances and Application of Machine Learning for Protein-Protein Interaction Prediction in Rice: Challenges and Future Perspectives.Proteomes · 2025Review
- Interpretable deep learning method to quantify the impact of extreme temperatures on vegetation productivity in China.Scientific reports · 2025Article
- Improving plant breeding through AI-supported data integration.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Review
- From text to traits: exploring the role of large language models in plant breeding.Frontiers in plant science · 2025Review
- A microbial knowledge graph-based deep learning model for predicting candidate microbes for target hosts.Briefings in bioinformatics · 2024Article
- Prediction of protein interactions between pine and pine wood nematode using deep learning and multi-dimensional feature fusion.Frontiers in plant science · 2024Article
- Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review.Molecules (Basel, Switzerland) · 2023Review
- Using PlaPPISite to Predict and Analyze Plant Protein-Protein Interaction Sites.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Exploring and Identifying Candidate Genes and Genomic Regions Related to Economically Important Traits in Hanwoo Cattle.Current issues in molecular biology · 2022Review
- A Novel Ensemble Learning-Based Computational Method to Predict Protein-Protein Interactions from Protein Primary Sequences.Biology · 2022Article
- KGDCMI: A New Approach for Predicting circRNA-miRNA Interactions From Multi-Source Information Extraction and Deep Learning.Frontiers in genetics · 2022Article
- BNEMDI: A Novel MicroRNA-Drug Interaction Prediction Model Based on Multi-Source Information With a Large-Scale Biological Network.Frontiers in genetics · 2022Article
- Overview of methods for characterization and visualization of a protein-protein interaction network in a multi-omics integration context.Frontiers in molecular biosciences · 2022Review
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8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The prediction of protein-protein interactions (PPIs) in plants is vital for probing the cell function. Although multiple high-throughput approaches in the biological domain have been developed to identify PPIs, with the increasing complexity of PPI network, these methods fall into laborious and time-consuming situations. Thus, it is essential to develop an effective and feasible computational method for the prediction of PPIs in plants. In this study, we present a network embedding-based method, called DWPPI, for predicting the interactions between different plant proteins based on multi-source information and combined with deep neural networks (DNN). The DWPPI model fuses the protein natural language sequence information (attribute information) and protein behavior information to represent plant proteins as feature vectors and finally sends these features to a deep learning-based classifier for prediction. To validate the prediction performance of DWPPI, we performed it on three model plant datasets:
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