PUBLICATION DATE

2011-11-30

TITLE

Dana-Farber repository for machine learning in immunology.

ISSUE

1-2

VOLUME

374

ISSN (print)

N/A

ISSN (electronic)

N/A

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/.

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JOURNAL BRAND

N/A (note: articles not published by Springer Nature have limited metadata)


FROM GRANT

  • Protective Orthopox Immunization In Normals And Patients With Cancer Or Eczema
  • Crossprotective Ctl Against Influenza
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    16 TRIPLES      15 PREDICATES      17 URIs      11 LITERALS

    Subject Predicate Object
    1 articles:f30cf7ec8be96b9dfa2dc31ec34ea65d sg: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/.
    2 sg:doi 10.1016/j.jim.2011.07.007
    3 sg:doiLink http://dx.doi.org/10.1016/j.jim.2011.07.007
    4 sg:isFundedPublicationOf grants:02b1aeb871004ea9d6388aac4bbaac36
    5 grants:1957cdb837e965371ccf578480e84561
    6 sg:issue 1-2
    7 sg:language English
    8 sg:license http://scigraph.springernature.com/explorer/license/
    9 sg:publicationDate 2011-11-30
    10 sg:publicationYear 2011
    11 sg:publicationYearMonth 2011-11
    12 sg:scigraphId f30cf7ec8be96b9dfa2dc31ec34ea65d
    13 sg:title Dana-Farber repository for machine learning in immunology.
    14 sg:volume 374
    15 rdf:type sg:Article
    16 rdfs:label Article: Dana-Farber repository for machine learning in immunology.
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