Dana-Farber repository for machine learning in immunology.

Abstract:

: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/.

journal_name

J Immunol Methods

authors

Zhang GL,Lin HH,Keskin DB,Reinherz EL,Brusic V

doi

10.1016/j.jim.2011.07.007

subject

Has Abstract

pub_date

2011-11-30 00:00:00

pages

18-25

issue

1-2

eissn

0022-1759

issn

1872-7905

pii

S0022-1759(11)00177-3

journal_volume

374

pub_type

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