کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
2088556 1545733 2011 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Dana-Farber repository for machine learning in immunology
موضوعات مرتبط
علوم زیستی و بیوفناوری بیوشیمی، ژنتیک و زیست شناسی مولکولی بیوتکنولوژی یا زیست‌فناوری
پیش نمایش صفحه اول مقاله
Dana-Farber repository for machine learning in immunology
چکیده انگلیسی

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/.


► The DFRMLI contains standardized HLA binding data.
► These data are prepared for development of machine learning algorithms.
► All binding affinities are mapped onto a common scale.
► DFRMLI data ensure consistency, comparability, and statistically meaningful data sets.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Immunological Methods - Volume 374, Issues 1–2, 30 November 2011, Pages 18–25
نویسندگان
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