Deep learning in next-generation vaccine development for infectious diseases.
Fuente:
PubMed
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Artículo científico |
| Langue: | en |
| Publié: |
Molecular therapy. Nucleic acids
2025
|
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1868266179237249026 |
|---|---|
| author | Bhattacharya, Manojit Lo, Yi-Hao Chatterjee, Srijan Das, Arpita Wen, Zhi-Hong Chakraborty, Chiranjib |
| author_facet | Bhattacharya, Manojit Lo, Yi-Hao Chatterjee, Srijan Das, Arpita Wen, Zhi-Hong Chakraborty, Chiranjib Bhattacharya, Manojit Lo, Yi-Hao Chatterjee, Srijan Das, Arpita Wen, Zhi-Hong Chakraborty, Chiranjib |
| collection | PubMed - marine biology |
| contents | Deep learning in next-generation vaccine development for infectious diseases. Bhattacharya, Manojit Lo, Yi-Hao Chatterjee, Srijan Das, Arpita Wen, Zhi-Hong Chakraborty, Chiranjib The landscape of vaccine development was changed in the genomic era with the help of computer science. Computer-aided vaccine epitope selection has become a foundation of rational vaccine design. Similarly, artificial intelligence (AI) is quickly transforming the vaccine development landscape. Deep learning (DL), a subset of AI, is used in the landscape of vaccine development in terms of its algorithms, tools, and technologies. This review article discussed the developmental history of the modern era of vaccine development strategies using both immunoinformatics with DL models, identification strategies of T cell epitopes and B cell epitopes through immunoinformatics and DL models, vaccine constructs development strategies using linker and adjuvant, and characterization strategies of vaccine construct using bioinformatics and immunoinformatics. Similarly, the article discusses different tools and technologies, from epitope mapping and vaccine construct development to characterization. Again, it also highlighted recent paradigm shifts, DL-based strategies in vaccine development, and different DL-based tools used for epitope mapping and vaccine construct development. However, integrated frameworks connecting the bioinformatics and DL approaches are rapidly progressing, which are necessary for DL-assisted epitope prediction and the subsequent steps for vaccine development. DL-assisted vaccine development is rapid and cost-effective, changing the scenario of next-generation vaccine development very fast. |
| format | Artículo científico |
| id | pubmed_40641804 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | Molecular therapy. Nucleic acids |
| record_format | pubmed |
| spellingShingle | Deep learning in next-generation vaccine development for infectious diseases. Bhattacharya, Manojit Lo, Yi-Hao Chatterjee, Srijan Das, Arpita Wen, Zhi-Hong Chakraborty, Chiranjib Deep learning in next-generation vaccine development for infectious diseases. Bhattacharya, Manojit Lo, Yi-Hao Chatterjee, Srijan Das, Arpita Wen, Zhi-Hong Chakraborty, Chiranjib The landscape of vaccine development was changed in the genomic era with the help of computer science. Computer-aided vaccine epitope selection has become a foundation of rational vaccine design. Similarly, artificial intelligence (AI) is quickly transforming the vaccine development landscape. Deep learning (DL), a subset of AI, is used in the landscape of vaccine development in terms of its algorithms, tools, and technologies. This review article discussed the developmental history of the modern era of vaccine development strategies using both immunoinformatics with DL models, identification strategies of T cell epitopes and B cell epitopes through immunoinformatics and DL models, vaccine constructs development strategies using linker and adjuvant, and characterization strategies of vaccine construct using bioinformatics and immunoinformatics. Similarly, the article discusses different tools and technologies, from epitope mapping and vaccine construct development to characterization. Again, it also highlighted recent paradigm shifts, DL-based strategies in vaccine development, and different DL-based tools used for epitope mapping and vaccine construct development. However, integrated frameworks connecting the bioinformatics and DL approaches are rapidly progressing, which are necessary for DL-assisted epitope prediction and the subsequent steps for vaccine development. DL-assisted vaccine development is rapid and cost-effective, changing the scenario of next-generation vaccine development very fast. |
| title | Deep learning in next-generation vaccine development for infectious diseases. |
| url | https://pubmed.ncbi.nlm.nih.gov/40641804/ |