ArticleBMC genomics2022
SDNN-PPI: self-attention with deep neural network effect on protein-protein interaction prediction.
Article in BMC genomics, 2022. 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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44 citing papers in PubMed, 80 citations in OpenAlex.
- Machine Learning and Deep Learning Frameworks for Human-Virus Protein-Protein Interaction Prediction: Emerging Architectures, Methods, Benchmarks, and Challenges.International journal of molecular sciences · 2026Review
- Protein language models are accidental taxonomists.BMC bioinformatics · 2026Article
- eSIG-Net: an interaction language model that decodes the protein code of single mutations.Nature methods · 2026Article
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- Predicting protein-protein interaction sites based on dynamic perception mechanism within a hierarchical E(n)-equivariant graph.Briefings in bioinformatics · 2026Article
- A Review and Experimental Analysis of Supervised Learning Systems and Methods for Protein-Protein Interaction Detection.International journal of molecular sciences · 2026Review
- ProVenTL: a transfer-learning framework for predicting peptide-protein interactions derived from snake venom for cancer therapeutics.Journal of computer-aided molecular design · 2026Article
- eSIG-Net: Accurate prediction of single-mutation induced perturbations on protein interactions using a language model.bioRxiv : the preprint server for biology · 2026Article
- Predicting Protein-Protein Interactions from Machine-Learned Representations.Advances in experimental medicine and biology · 2026Review
- Protein Language Models are Accidental Taxonomists.bioRxiv : the preprint server for biology · 2025Article
- Sequence-Based Protein-Protein Interaction Prediction and Its Applications in Drug Discovery.Cells · 2025Review
- Bias-aware training and evaluation of link prediction algorithms in network biology.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Recent advances in deep learning for protein-protein interaction: a review.BioData mining · 2025Review
- Cancer network pharmacology: multi-network regulatory mechanisms and future directions.Medical oncology (Northwood, London, England) · 2025Review
- Enhancing molecular property prediction with quantized GNN models.Journal of cheminformatics · 2025Article
- Negative sampling strategies impact the prediction of scale-free biomolecular network interactions with machine learning.BMC biology · 2025Article
- Effect of acupuncture on brain activity in patients with decreasing ovarian reserve: a resting-state functional magnetic resonance imaging study.Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2025Article
- TGF-M: Topology-augmented geometric features enhance molecular property prediction.PLoS computational biology · 2025Article
- PPILS: Protein-protein interaction prediction with language of biological coding.Computers in biology and medicine · 2025Article
- UniAMP: enhancing AMP prediction using deep neural networks with inferred information of peptides.BMC bioinformatics · 2025Article
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6 authors at 1 institution in 1 country.
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Abstract
backgroundProtein-protein interactions (PPIs) dominate intracellular molecules to perform a series of tasks such as transcriptional regulation, information transduction, and drug signalling. The traditional wet experiment method to obtain PPIs information is costly and time-consuming.
resultIn this paper, SDNN-PPI, a PPI prediction method based on self-attention and deep learning is proposed. The method adopts amino acid composition (AAC), conjoint triad (CT), and auto covariance (AC) to extract global and local features of protein sequences, and leverages self-attention to enhance DNN feature extraction to more effectively accomplish the prediction of PPIs. In order to verify the generalization ability of SDNN-PPI, a 5-fold cross-validation on the intraspecific interactions dataset of Saccharomyces cerevisiae (core subset) and human is used to measure our model in which the accuracy reaches 95.48% and 98.94% respectively. The accuracy of 93.15% and 88.33% are obtained in the interspecific interactions dataset of human-Bacillus Anthracis and Human-Yersinia pestis, respectively. In the independent data set Caenorhabditis elegans, Escherichia coli, Homo sapiens, and Mus musculus, all prediction accuracy is 100%, which is higher than the previous PPIs prediction methods. To further evaluate the advantages and disadvantages of the model, the one-core and crossover network are conducted to predict PPIs, and the data show that the model correctly predicts the interaction pairs in the network.
conclusionIn this paper, AAC, CT and AC methods are used to encode the sequence, and SDNN-PPI method is proposed to predict PPIs based on self-attention deep learning neural network. Satisfactory results are obtained on interspecific and intraspecific data sets, and good performance is also achieved in cross-species prediction. It can also correctly predict the protein interaction of cell and tumor information contained in one-core network and crossover network.The SDNN-PPI proposed in this paper not only explores the mechanism of protein-protein interaction, but also provides new ideas for drug design and disease prevention.
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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.