ArticleScientific reports2015
A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks.
Article in Scientific reports, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- APBIO: bioactive profiling of air pollutants through inferred bioactivity signatures and prediction of novel target interactions.Journal of cheminformatics · 2025Article
- Predictive model for totally implanted venous access ports‑related long‑term complications in patients with lung cancer.Oncology letters · 2024Article
- Bioinformatics approaches for unveiling virus-host interactions.Computational and structural biotechnology journal · 2023Review
- A multitask transfer learning framework for the prediction of virus-human protein-protein interactions.BMC bioinformatics · 2021Article
- In silico unravelling pathogen-host signaling cross-talks via pathogen mimicry and human protein-protein interaction networks.Computational and structural biotechnology journal · 2020Article
- A Bipartite Network Module-Based Project to Predict Pathogen-Host Association.Frontiers in genetics · 2019Article
- 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
- Assisting document triage for human kinome curation via machine learning.Database : the journal of biological databases and curation · 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
- Uncovering New Pathogen-Host Protein-Protein Interactions by Pairwise Structure Similarity.PloS one · 2016Article
- ONE-CLASS DETECTION OF CELL STATES IN TUMOR SUBTYPES.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2016Article
- A simple feature construction method for predicting upstream/downstream signal flow in human protein-protein interaction networks.Scientific reports · 2015Article
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2 authors.
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Abstract
Protein-protein interaction (PPI) prediction is generally treated as a problem of binary classification wherein negative data sampling is still an open problem to be addressed. The commonly used random sampling is prone to yield less representative negative data with considerable false negatives. Meanwhile rational constraints are seldom exerted on model selection to reduce the risk of false positive predictions for most of the existing computational methods. In this work, we propose a novel negative data sampling method based on one-class SVM (support vector machine, SVM) to predict proteome-wide protein interactions between HTLV retrovirus and Homo sapiens, wherein one-class SVM is used to choose reliable and representative negative data, and two-class SVM is used to yield proteome-wide outcomes as predictive feedback for rational model selection. Computational results suggest that one-class SVM is more suited to be used as negative data sampling method than two-class PPI predictor, and the predictive feedback constrained model selection helps to yield a rational predictive model that reduces the risk of false positive predictions. Some predictions have been validated by the recent literature. Lastly, gene ontology based clustering of the predicted PPI networks is conducted to provide valuable cues for the pathogenesis of HTLV retrovirus.
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