免疫疗法
癌症免疫疗法
计算生物学
优先次序
离体
表位
癌症
鉴定(生物学)
选择(遗传算法)
生物
医学
免疫学
抗原
计算机科学
体内
机器学习
遗传学
经济
管理科学
植物
作者
Catarina Nogueira,Johanna K. Kaufmann,Hubert Lam,Jessica B. Flechtner
标识
DOI:10.1016/j.trecan.2017.12.003
摘要
Targeting neoantigens has become an attractive strategy for cancer immunotherapy. Epitope prediction algorithms facilitate rapid selection of potential neoantigens, but are plagued with high false-positive and false-negative rates. Here we review ex vivo technologies for biological identification of neoantigens to improve empirical prioritization for immunotherapy. Targeting neoantigens has become an attractive strategy for cancer immunotherapy. Epitope prediction algorithms facilitate rapid selection of potential neoantigens, but are plagued with high false-positive and false-negative rates. Here we review ex vivo technologies for biological identification of neoantigens to improve empirical prioritization for immunotherapy.
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