Dana-Farber repository for machine learning in immunology.
Dana-Farber repository for machine learning in immunology.
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DOI：
10.1016/j.jim.2011.07.007
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发表时间：
2011-11-30
影响因子：
2.2
通讯作者：
Brusic, Vladimir
中科院分区：
文献类型：
--
作者：
Zhang, Guang Lan;Lin, Hong Huang;Keskin, Derin B.;Reinherz, Ellis L.;Brusic, Vladimir
免疫系统的特点是组合的复杂性很高，这就需要使用专门的计算工具来分析免疫学数据。机器学习(ML)算法与经典实验相结合，用于选择疫苗目标和计算模拟，以减少必要的实验数量。ML算法的发展需要标准化的数据集、一致的测量方法和统一的标度。为了弥合免疫学社区和ML社区之间的差距，我们设计了一个免疫学机器学习知识库，名为Dana-Farber免疫学机器学习知识库(DFRMLI)。这个库提供了标准化的人类白细胞抗原结合多肽数据集，所有结合亲和力都映射到一个公共标尺上。它还提供了一份经过实验验证的来自肿瘤或病毒抗原的自然加工T细胞表位的清单。DFRMLI数据经过了预处理，确保了与各种人类白细胞抗原分子结合的多肽的一致性、可比性、详细描述和具有统计学意义的样本量。该存储库可在http://bio.dfci.harvard.edu/DFRMLI/.上访问
The immune system is characterized by high combinatorial complexity that necessitates the use of specialized computational tools for analysis of immunological data. Machine learning (ML) algorithms are used in combination with classical experimentation for the selection of vaccine targets and in computational simulations that reduce the number of necessary experiments. The development of ML algorithms requires standardized data sets, consistent measurement methods, and uniform scales. To bridge the gap between the immunology community and the ML community, we designed a repository for machine learning in immunology named Dana-Farber Repository for Machine Learning in Immunology (DFRMLI). This repository provides standardized data sets of HLA-binding peptides with all binding affinities mapped onto a common scale. It also provides a list of experimentally validated naturally processed T cell epitopes derived from tumor or virus antigens. The DFRMLI data were preprocessed and ensure consistency, comparability, detailed descriptions, and statistically meaningful sample sizes for peptides that bind to various HLA molecules. The repository is accessible at http://bio.dfci.harvard.edu/DFRMLI/.
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通讯作者：
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