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A framework for predicting variable-length epitopes of human-adapted viruses using machine learning methods.
Yin, Rui; Zhu, Xianghe; Zeng, Min; Wu, Pengfei; Li, Min; Kwoh, Chee Keong.
Affiliation
  • Yin R; Department of Biomedical Informatics, Harvard Medical School, Boston, USA.
  • Zhu X; Department of Statistics, University of Oxford, Oxford, UK.
  • Zeng M; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China.
  • Wu P; Center for Medical Genetics, School of Life Sciences, Central South University, Changsha, China.
  • Li M; Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China.
  • Kwoh CK; School of Computer Science and Engineering, Nanyang Technological University, Singapore.
Brief Bioinform ; 23(5)2022 09 20.
Article in En | MEDLINE | ID: mdl-35849093
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Viruses / COVID-19 Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Brief Bioinform Journal subject: BIOLOGIA / INFORMATICA MEDICA Year: 2022 Document type: Article Affiliation country: Estados Unidos Country of publication: Reino Unido
Fulltext
Add to My VHL
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Viruses / COVID-19 Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Brief Bioinform Journal subject: BIOLOGIA / INFORMATICA MEDICA Year: 2022 Document type: Article Affiliation country: Estados Unidos Country of publication: Reino Unido