ArticlePloS one2013
Probability weighted ensemble transfer learning for predicting interactions between HIV-1 and human proteins.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Computational Approach for Detection and Analysis of Human-Virus Protein-Protein Interactions.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Deep Learning for Genomics: From Early Neural Nets to Modern Large Language Models.International journal of molecular sciences · 2023Review
- Segmentation-Assisted Fully Convolutional Neural Network Enhances Deep Learning Performance to Identify Proliferative Diabetic Retinopathy.Journal of clinical medicine · 2023Article
- In silico prediction of HIV-1-host molecular interactions and their directionality.PLoS computational biology · 2022Article
- A multitask transfer learning framework for the prediction of virus-human protein-protein interactions.BMC bioinformatics · 2021Article
- Prediction of host-pathogen protein interactions by extended network model.Turkish journal of biology = Turk biyoloji dergisi · 2021Article
- Computational Biology and Machine Learning Approaches to Understand Mechanistic Microbiome-Host Interactions.Frontiers in microbiology · 2021Review
- SARS-CoV-2-human protein-protein interaction network.Informatics in medicine unlocked · 2020Article
- In silico unravelling pathogen-host signaling cross-talks via pathogen mimicry and human protein-protein interaction networks.Computational and structural biotechnology journal · 2020Article
- Neglog: Homology-Based Negative Data Sampling Method for Genome-Scale Reconstruction of Human Protein-Protein Interaction Networks.International journal of molecular sciences · 2019Article
- Machine-learning techniques for the prediction of protein-protein interactions.Journal of biosciences · 2019Article
- Review of computational methods for virus-host protein interaction prediction: a case study on novel Ebola-human interactions.Briefings in functional genomics · 2018Review
- Context Impacts in Accelerometer-Based Walk Detection and Step Counting.Sensors (Basel, Switzerland) · 2018Article
- Inter-Species/Host-Parasite Protein Interaction Predictions Reviewed.Current bioinformatics · 2018Review
- Transferring knowledge of bacterial protein interaction networks to predict pathogen targeted human genes and immune signaling pathways: a case study on M. tuberculosis.BMC genomics · 2018Article
- Annotating activation/inhibition relationships to protein-protein interactions using gene ontology relations.BMC systems biology · 2018Article
- A computational framework for distinguishing direct versus indirect interactions in human functional protein-protein interaction networks.Integrative biology : quantitative biosciences from nano to macro · 2017Article
- Multi-label ℓScientific reports · 2016Article
- Computational discovery of Epstein-Barr virus targeted human genes and signalling pathways.Scientific reports · 2016Article
- Multi-label multi-instance transfer learning for simultaneous reconstruction and cross-talk modeling of multiple human signaling pathways.BMC bioinformatics · 2015Article
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1 author.
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Abstract
Reconstruction of host-pathogen protein interaction networks is of great significance to reveal the underlying microbic pathogenesis. However, the current experimentally-derived networks are generally small and should be augmented by computational methods for less-biased biological inference. From the point of view of computational modelling, data scarcity, data unavailability and negative data sampling are the three major problems for host-pathogen protein interaction networks reconstruction. In this work, we are motivated to address the three concerns and propose a probability weighted ensemble transfer learning model for HIV-human protein interaction prediction (PWEN-TLM), where support vector machine (SVM) is adopted as the individual classifier of the ensemble model. In the model, data scarcity and data unavailability are tackled by homolog knowledge transfer. The importance of homolog knowledge is measured by the ROC-AUC metric of the individual classifiers, whose outputs are probability weighted to yield the final decision. In addition, we further validate the assumption that only the homolog knowledge is sufficient to train a satisfactory model for host-pathogen protein interaction prediction. Thus the model is more robust against data unavailability with less demanding data constraint. As regards with negative data construction, experiments show that exclusiveness of subcellular co-localized proteins is unbiased and more reliable than random sampling. Last, we conduct analysis of overlapped predictions between our model and the existing models, and apply the model to novel host-pathogen PPIs recognition for further biological research.
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