ArticleBMC bioinformatics2013
A novel strategy for classifying the output from an in silico vaccine discovery pipeline for eukaryotic pathogens using machine learning algorithms.
Article in BMC bioinformatics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Plasmodium vivax antigen candidate prediction improves with the addition of Plasmodium falciparum data.NPJ systems biology and applications · 2024Article
- Positive-unlabeled learning identifies vaccine candidate antigens in the malaria parasite Plasmodium falciparum.NPJ systems biology and applications · 2024Article
- Compilation of parasitic immunogenic proteins from 30 years of published research using machine learning and natural language processing.Scientific reports · 2022Article
- Machine Learning and Its Applications for Protozoal Pathogens and Protozoal Infectious Diseases.Frontiers in cellular and infection microbiology · 2022Review
- Machine learning and applications in microbiology.FEMS microbiology reviews · 2021Review
- Identification of anti-horn fly vaccine antigen candidates using a reverse vaccinology approach.Parasites & vectors · 2021Article
- Identification of vaccine targets in pathogens and design of a vaccine using computational approaches.Scientific reports · 2021Article
- Predicting Protein Therapeutic Candidates for Bovine Babesiosis Using Secondary Structure Properties and Machine Learning.Frontiers in genetics · 2021Article
- Evaluation of an ensemble-based distance statistic for clustering MLST datasets using epidemiologically defined clusters of cyclosporiasis.Epidemiology and infection · 2020Article
- Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens.Bioinformatics (Oxford, England) · 2020Article
- Exploitation of reverse vaccinology and immunoinformatics as promising platform for genome-wide screening of new effective vaccine candidates against Plasmodium falciparum.BMC bioinformatics · 2019Article
- A Gene-Based Positive Selection Detection Approach to Identify Vaccine Candidates UsingFrontiers in genetics · 2018Article
- The genome of the protozoan parasite Cystoisospora suis and a reverse vaccinology approach to identify vaccine candidates.International journal for parasitology · 2017Article
- Identification of New Features from Known Bacterial Protective Vaccine Antigens Enhances Rational Vaccine Design.Frontiers in immunology · 2017Article
- Boosting for high-dimensional two-class prediction.BMC bioinformatics · 2015Article
- Cystoisospora suis - A Model of Mammalian Cystoisosporosis.Frontiers in veterinary science · 2015Review
- Vacceed: a high-throughput in silico vaccine candidate discovery pipeline for eukaryotic pathogens based on reverse vaccinology.Bioinformatics (Oxford, England) · 2014Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAn in silico vaccine discovery pipeline for eukaryotic pathogens typically consists of several computational tools to predict protein characteristics. The aim of the in silico approach to discovering subunit vaccines is to use predicted characteristics to identify proteins which are worthy of laboratory investigation. A major challenge is that these predictions are inherent with hidden inaccuracies and contradictions. This study focuses on how to reduce the number of false candidates using machine learning algorithms rather than relying on expensive laboratory validation. Proteins from Toxoplasma gondii, Plasmodium sp., and Caenorhabditis elegans were used as training and test datasets.
resultsThe results show that machine learning algorithms can effectively distinguish expected true from expected false vaccine candidates (with an average sensitivity and specificity of 0.97 and 0.98 respectively), for proteins observed to induce immune responses experimentally.
conclusionsVaccine candidates from an in silico approach can only be truly validated in a laboratory. Given any in silico output and appropriate training data, the number of false candidates allocated for validation can be dramatically reduced using a pool of machine learning algorithms. This will ultimately save time and money in the laboratory.
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