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An ensemble method for prediction of phage-based therapy against bacterial infections.
Aggarwal, Suchet; Dhall, Anjali; Patiyal, Sumeet; Choudhury, Shubham; Arora, Akanksha; Raghava, Gajendra P S.
Afiliación
  • Aggarwal S; Department of Computer Science and Engineering, Indraprastha Institute of Information Technology, New Delhi, India.
  • Dhall A; Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
  • Patiyal S; Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
  • Choudhury S; Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
  • Arora A; Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
  • Raghava GPS; Department of Computational Biology, Indraprastha Institute of Information Technology, New Delhi, India.
Front Microbiol ; 14: 1148579, 2023.
Article en En | MEDLINE | ID: mdl-37032893
Phage therapy is a viable alternative to antibiotics for treating microbial infections, particularly managing drug-resistant strains of bacteria. One of the major challenges in designing phage-based therapy is to identify the most appropriate potential phage candidate to treat bacterial infections. In this study, an attempt has been made to predict phage-host interactions with high accuracy to identify the potential bacteriophage that can be used for treating a bacterial infection. The developed models have been created using a training dataset containing 826 phage- host interactions, and have been evaluated on a validation dataset comprising 1,201 phage-host interactions. Firstly, alignment-based models have been developed using similarity between phage-phage (BLASTPhage), host-host (BLASTHost) and phage-CRISPR (CRISPRPred), where we achieved accuracy between 42.4-66.2% for BLASTPhage, 55-78.4% for BLASTHost, and 43.7-80.2% for CRISPRPred across five taxonomic levels. Secondly, alignment free models have been developed using machine learning techniques. Thirdly, hybrid models have been developed by integrating the alignment-free models and the similarity-scores where we achieved maximum performance of (60.6-93.5%). Finally, an ensemble model has been developed that combines the hybrid and alignment-based models. Our ensemble model achieved highest accuracy of 67.9, 80.6, 85.5, 90, and 93.5% at Genus, Family, Order, Class, and Phylum levels on validation dataset. In order to serve the scientific community, we have also developed a webserver named PhageTB and provided a standalone software package (https://webs.iiitd.edu.in/raghava/phagetb/) for the same.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Microbiol Año: 2023 Tipo del documento: Article País de afiliación: India Pais de publicación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Microbiol Año: 2023 Tipo del documento: Article País de afiliación: India Pais de publicación: Suiza