ArticlePloS one2012
A comparison of computational methods for identifying virulence factors.
Article in PloS one, 2012. 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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Who cites it
20 citing papers in PubMed, 38 citations in OpenAlex.
- SEVA: structural and evolutionary feature integration for predicting virulence factors and antibiotic resistance genes.Microbiome · 2026Article
- Co-expression network analysis and systems-level mapping of hub-bottleneck regulators deciphers the virulence architecture of Helicobacter pylori.Archives of microbiology · 2026Article
- Advancing virulence factor prediction using protein language models.BMC biology · 2025Article
- Prediction of new candidate proteins and analysis of sub-modules and protein hubs associated with seed development in rice (Oryza sativa) using an ensemble network-based systems biology approach.BMC plant biology · 2025Article
- Article
- Pathogenomic in silico approach identifies NSP-A and Fe-IIISBP as possible drug targets in Neisseria Meningitidis MC58 and development of pharmacophores as novel therapeutic candidates.Molecular diversity · 2023Article
- Challenges in Drug Discovery for Intracellular Bacteria.Pathogens (Basel, Switzerland) · 2021Article
- Network-based analysis of virulence factors for uncovering Aeromonas veronii pathogenesis.BMC microbiology · 2021Article
- DeepVF: a deep learning-based hybrid framework for identifying virulence factors using the stacking strategy.Briefings in bioinformatics · 2021Article
- An Improved Approach to Identify Bacterial Pathogens to Human in Environmental Metagenome.Journal of microbiology and biotechnology · 2020Article
- Genome-scale metabolic reconstruction and in silico analysis of the rice leaf blight pathogen, Xanthomonas oryzae.Molecular plant pathology · 2020Article
- Typing methods based on whole genome sequencing data.One health outlook · 2020Review
- Implications of Newly Identified Brain eQTL Genes and Their Interactors in Schizophrenia.Molecular therapy. Nucleic acids · 2018Article
- Small Myristoylated Protein-3, Identified as a Potential Virulence Factor in Leishmania amazonensis, Proves to be a Protective Antigen against Visceral Leishmaniasis.International journal of molecular sciences · 2018Article
- Prediction of the aquatic toxicity of aromatic compounds to tetrahymena pyriformis through support vector regression.Oncotarget · 2017Article
- Virulence factor activity relationships (VFARs): a bioinformatics perspective.Environmental science. Processes & impacts · 2017Review
- Identification of functional candidates amongst hypothetical proteins of Treponema pallidum ssp. pallidum.PloS one · 2015Article
- Novel bacterial ADP-ribosylating toxins: structure and function.Nature reviews. Microbiology · 2014Review
- Molecular characterization of putative virulence determinants in Burkholderia pseudomallei.TheScientificWorldJournal · 2014Article
- Latent Tuberculosis: Models, Computational Efforts and the Pathogen's Regulatory Mechanisms during Dormancy.Frontiers in bioengineering and biotechnology · 2013Review
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Bacterial pathogens continue to threaten public health worldwide today. Identification of bacterial virulence factors can help to find novel drug/vaccine targets against pathogenicity. It can also help to reveal the mechanisms of the related diseases at the molecular level. With the explosive growth in protein sequences generated in the postgenomic age, it is highly desired to develop computational methods for rapidly and effectively identifying virulence factors according to their sequence information alone. In this study, based on the protein-protein interaction networks from the STRING database, a novel network-based method was proposed for identifying the virulence factors in the proteomes of UPEC 536, UPEC CFT073, P. aeruginosa PAO1, L. pneumophila Philadelphia 1, C. jejuni NCTC 11168 and M. tuberculosis H37Rv. Evaluated on the same benchmark datasets derived from the aforementioned species, the identification accuracies achieved by the network-based method were around 0.9, significantly higher than those by the sequence-based methods such as BLAST, feature selection and VirulentPred. Further analysis showed that the functional associations such as the gene neighborhood and co-occurrence were the primary associations between these virulence factors in the STRING database. The high success rates indicate that the network-based method is quite promising. The novel approach holds high potential for identifying virulence factors in many other various organisms as well because it can be easily extended to identify the virulence factors in many other bacterial species, as long as the relevant significant statistical data are available for them.
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Registered trials
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