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Applying Machine Learning to Predict the Exportome of Bovine and Canine Babesia Species That Cause Babesiosis
- Source :
- Pathogens, Vol 10, Iss 660, p 660 (2021), Pathogens, Volume 10, Issue 6
- Publication Year :
- 2021
- Publisher :
- MDPI AG, 2021.
-
Abstract
- Babesia infection of red blood cells can cause a severe disease called babesiosis in susceptible hosts. Bovine babesiosis causes global economic loss to the beef and dairy cattle industries, and canine babesiosis is considered a clinically significant disease. Potential therapeutic targets against bovine and canine babesiosis include members of the exportome, i.e., those proteins exported from the parasite into the host red blood cell. We developed three machine learning-derived methods (two novel and one adapted) to predict for every known Babesia bovis, Babesia bigemina, and Babesia canis protein the probability of being an exportome member. Two well-studied apicomplexan-related species, Plasmodium falciparum and Toxoplasma gondii, with extensive experimental evidence on their exportome or excreted/secreted proteins were used as important benchmarks for the three methods. Based on 10-fold cross validation and multiple train–validation–test splits of training data, we expect that over 90% of the predicted probabilities accurately provide a secretory or non-secretory indicator. Only laboratory testing can verify that predicted high exportome membership probabilities are creditable exportome indicators. However, the presented methods at least provide those proteins most worthy of laboratory validation and will ultimately save time and money.
- Subjects :
- Microbiology (medical)
exportome
Babesia canis
excreted/secreted proteins
parasitic diseases
medicine
1107 Immunology, 1108 Medical Microbiology
Immunology and Allergy
Parasite hosting
Molecular Biology
Babesia bigemina
Dairy cattle
General Immunology and Microbiology
biology
babesiosis
Toxoplasma gondii
Babesia bovis
Babesiosis
Plasmodium falciparum
biology.organism_classification
medicine.disease
Virology
Infectious Diseases
Medicine
Subjects
Details
- Language :
- English
- ISSN :
- 20760817
- Volume :
- 10
- Issue :
- 660
- Database :
- OpenAIRE
- Journal :
- Pathogens
- Accession number :
- edsair.doi.dedup.....db8ad052d4c5da70d82da5ee6708fb4c