ArticleBMC bioinformatics2021
A multitask transfer learning framework for the prediction of virus-human protein-protein interactions.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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20 citing papers in PubMed, 33 citations in OpenAlex.
- Comprehensive review and assessment of machine learning approaches for host-pathogen protein-protein interaction prediction.Briefings in bioinformatics · 2026Review
- MPIDNN-GPPI: multi-protein language model with an improved deep neural network for generalized protein‒protein interaction prediction.BMC genomics · 2025Article
- Sequence-Based Protein-Protein Interaction Prediction and Its Applications in Drug Discovery.Cells · 2025Review
- Medium-sized protein language models perform well at transfer learning on realistic datasets.Scientific reports · 2025Article
- Medium-sized protein language models perform well at transfer learning on realistic datasets.bioRxiv : the preprint server for biology · 2025Article
- Unveiling the ghost: machine learning's impact on the landscape of virology.The Journal of general virology · 2025Review
- Multitask Learning-Based Approaches for Protein Function Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Nanobody screening and machine learning guided identification of cross-variant anti-SARS-CoV-2 neutralizing heavy-chain only antibodies.PLoS pathogens · 2025Article
- Bioinformatic Resources for Exploring Human-virus Protein-protein Interactions Based on Binding Modes.Genomics, proteomics & bioinformatics · 2024Review
- Zero-shot prediction of mutation effects with multimodal deep representation learning guides protein engineering.Cell research · 2024Article
- MpbPPI: a multi-task pre-training-based equivariant approach for the prediction of the effect of amino acid mutations on protein-protein interactions.Briefings in bioinformatics · 2023Article
- Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review.Molecules (Basel, Switzerland) · 2023Review
- Bioinformatics approaches for unveiling virus-host interactions.Computational and structural biotechnology journal · 2023Review
- Recent developments of sequence-based prediction of protein-protein interactions.Biophysical reviews · 2022Review
- Transfer learning for genotype-phenotype prediction using deep learning models.BMC bioinformatics · 2022Article
- HMI-PRED 2.0: a biologist-oriented web application for prediction of host-microbe protein-protein interaction by interface mimicry.Bioinformatics (Oxford, England) · 2022Article
- Deep learning frameworks for protein-protein interaction prediction.Computational and structural biotechnology journal · 2022Review
- Deep Learning-Powered Prediction of Human-Virus Protein-Protein Interactions.Frontiers in microbiology · 2022Article
- MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses.Frontiers in medicine · 2022Article
- A multitask transfer learning framework for the prediction of virus-human protein-protein interactions.BMC bioinformatics · 2021Article
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
Abstract
backgroundViral infections are causing significant morbidity and mortality worldwide. Understanding the interaction patterns between a particular virus and human proteins plays a crucial role in unveiling the underlying mechanism of viral infection and pathogenesis. This could further help in prevention and treatment of virus-related diseases. However, the task of predicting protein-protein interactions between a new virus and human cells is extremely challenging due to scarce data on virus-human interactions and fast mutation rates of most viruses.
resultsWe developed a multitask transfer learning approach that exploits the information of around 24 million protein sequences and the interaction patterns from the human interactome to counter the problem of small training datasets. Instead of using hand-crafted protein features, we utilize statistically rich protein representations learned by a deep language modeling approach from a massive source of protein sequences. Additionally, we employ an additional objective which aims to maximize the probability of observing human protein-protein interactions. This additional task objective acts as a regularizer and also allows to incorporate domain knowledge to inform the virus-human protein-protein interaction prediction model.
conclusionsOur approach achieved competitive results on 13 benchmark datasets and the case study for the SARS-COV-2 virus receptor. Experimental results show that our proposed model works effectively for both virus-human and bacteria-human protein-protein interaction prediction tasks. We share our code for reproducibility and future research at https://git.l3s.uni-hannover.de/dong/multitask-transfer .
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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 Socratic graph.