ArticleBioinformatics (Oxford, England)2019
Multifaceted protein-protein interaction prediction based on Siamese residual RCNN.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 152 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
152 citing papers in PubMed.
- R1-32-like public antibodies acquire tolerance to SARS-CoV-2 antigenic drift through somatic hypermutation.PLoS biology · 2026Article
- Decoupling topological and molecular features for interpretable biomolecular interaction prediction.Briefings in bioinformatics · 2026Article
- Resnet-Driven In Silico Identification of Lead Peptides from the Venom Gland Transcriptome ofPharmaceuticals (Basel, Switzerland) · 2026Article
- The limits of bio-molecular modeling with large language models: a cross-scale evaluation.Bioinformatics (Oxford, England) · 2026Article
- 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
- Towards the construction of a virtual yeast.Nature · 2026Review
- A multimodal human protein embeddings database: DeepDrug Protein Embeddings Bank (DPEB).NAR genomics and bioinformatics · 2026Article
- eSIG-Net: an interaction language model that decodes the protein code of single mutations.Nature methods · 2026Article
- ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Identifying potential ligand-receptor interactions by integrating LSTM network and the attention mechanism for cell-cell communication prediction.Journal of translational medicine · 2026Article
- Interpretable deep learning framework for mapping E3-substrate binding interfaces.Nature communications · 2026Article
- ProSSF: integrating sequence, structure, and gene ontology for prediction of protein stability, interaction, and function.Molecular genetics and genomics : MGG · 2026Article
- A Bimodal Graph Neural Network with Transfer Learning and Contrastive Learning for Protein-Protein Interaction Site Prediction.Interdisciplinary sciences, computational life sciences · 2026Article
- Accessibility in proteins and RNAs interactions prediction with machine learning: are we overlooking non-experts?Briefings in bioinformatics · 2026Review
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- Rapid Proteome-Wide Discovery of Protein-Protein Interactions With ppIRIS.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Dual-channel graph learning reveals similarity and complementarity in protein-protein interaction networks.PLoS computational biology · 2026Article
- eSIG-Net: Accurate prediction of single-mutation induced perturbations on protein interactions using a language model.bioRxiv : the preprint server for biology · 2026Article
- DeepISO: deep learning-powered prediction of protein-protein interaction rewiring generated by alternative splicing.Genome biology · 2026Article
- DSS-PPI: a self-supervised graph learning framework for protein-protein interaction prediction via multimodal sequence semantics.BMC genomics · 2026Article
92 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
motivationSequence-based protein-protein interaction (PPI) prediction represents a fundamental computational biology problem. To address this problem, extensive research efforts have been made to extract predefined features from the sequences. Based on these features, statistical algorithms are learned to classify the PPIs. However, such explicit features are usually costly to extract, and typically have limited coverage on the PPI information.
resultsWe present an end-to-end framework, PIPR (Protein-Protein Interaction Prediction Based on Siamese Residual RCNN), for PPI predictions using only the protein sequences. PIPR incorporates a deep residual recurrent convolutional neural network in the Siamese architecture, which leverages both robust local features and contextualized information, which are significant for capturing the mutual influence of proteins sequences. PIPR relieves the data pre-processing efforts that are required by other systems, and generalizes well to different application scenarios. Experimental evaluations show that PIPR outperforms various state-of-the-art systems on the binary PPI prediction problem. Moreover, it shows a promising performance on more challenging problems of interaction type prediction and binding affinity estimation, where existing approaches fall short. AVAILABILITY AND IMPLEMENTATION: The implementation is available at https://github.com/muhaochen/seq_ppi.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Indexed as
Identifiers
What OpenQuestion holds
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.