A comparative study of multiple instance learning methods for cancer detection using T-cell receptor sequences

As a branch of machine learning, multiple instance learning (MIL) learns from a collection of labeled bags, each containing a set of instances. The learning process is weakly supervised due to ambiguous instance labels. Since its emergence, MIL has been applied to solve various problems including co...

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Detalles Bibliográficos
Autores principales: Xiong, Danyi, Zhang, Ze, Wang, Tao, Wang, Xinlei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2021
Materias:
Review
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8192570/
https://www.ncbi.nlm.nih.gov/pubmed/34141144
http://dx.doi.org/10.1016/j.csbj.2021.05.038

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8192570/
https://www.ncbi.nlm.nih.gov/pubmed/34141144
http://dx.doi.org/10.1016/j.csbj.2021.05.038

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