MaHCO: an ontology of the major histocompatibility complex for immunoinformatic applications and text mining

DeLuca, David S.; Beisswanger, Elena; Wermter, Joachim; Horn, Peter A.; Hahn, Udo; Blasczyk, Rainer

doi:10.1093/bioinformatics/btp306

Cited by 8 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…NCBO Annotator produces very low recall (R = 0.02) and performs poorly due to a combination of: the way CRAFT is annotated and the way NCBO Annotator handles linking between ontologies. In NCBO Annotator, for example, the link between “mice” and “Mus” is not inferred directly, but goes through the MaHCO ontology [55], an ontology of major histocompatibility complexes. Because we limited NCBO Annotator to only using ontology directly tested, the link between “mice” and “Mus” is not used, and therefore are not found.…”

Section: Resultsmentioning

confidence: 99%

Large-scale biomedical concept recognition: an evaluation of current automatic annotators and their parameters

Funk¹,

Baumgartner²,

et al. 2014

View full text Add to dashboard Cite

BackgroundOntological concepts are useful for many different biomedical tasks. Concepts are difficult to recognize in text due to a disconnect between what is captured in an ontology and how the concepts are expressed in text. There are many recognizers for specific ontologies, but a general approach for concept recognition is an open problem.ResultsThree dictionary-based systems (MetaMap, NCBO Annotator, and ConceptMapper) are evaluated on eight biomedical ontologies in the Colorado Richly Annotated Full-Text (CRAFT) Corpus. Over 1,000 parameter combinations are examined, and best-performing parameters for each system-ontology pair are presented.ConclusionsBaselines for concept recognition by three systems on eight biomedical ontologies are established (F-measures range from 0.14–0.83). Out of the three systems we tested, ConceptMapper is generally the best-performing system; it produces the highest F-measure of seven out of eight ontologies. Default parameters are not ideal for most systems on most ontologies; by changing parameters F-measure can be increased by up to 0.4. Not only are best performing parameters presented, but suggestions for choosing the best parameters based on ontology characteristics are presented.

show abstract

Section: Resultsmentioning

confidence: 99%

Large-scale biomedical concept recognition: an evaluation of current automatic annotators and their parameters

Funk¹,

Baumgartner²,

et al. 2014

View full text Add to dashboard Cite

show abstract

“…We set out to logically represent the relationships between the genes encoding MHC, the haplotypes linking together groups of genes in specific species, and the individual proteins comprising MHC complexes, in order to present immunological data in an exact way and to improve the functionality of our website. Our work builds on MaHCO [ 3 ], an ontology for MHC developed for the StemNet project, using the well-established MHC nomenclature resources of the international ImMunoGeneTics information system (IMGT, http://www.imgt.org ) for human data and The Immuno Polymorphism Database (IPD, http://www.ebi.ac.uk/ipd ) for non-human species. It contains 118 terms for MHC across human, mouse, and dog.…”

Section: Methodsmentioning

confidence: 99%

An ontology for major histocompatibility restriction

et al. 2016

View full text Add to dashboard Cite

BackgroundMHC molecules are a highly diverse family of proteins that play a key role in cellular immune recognition. Over time, different techniques and terminologies have been developed to identify the specific type(s) of MHC molecule involved in a specific immune recognition context. No consistent nomenclature exists across different vertebrate species.PurposeTo correctly represent MHC related data in The Immune Epitope Database (IEDB), we built upon a previously established MHC ontology and created an ontology to represent MHC molecules as they relate to immunological experiments.DescriptionThis ontology models MHC protein chains from 16 species, deals with different approaches used to identify MHC, such as direct sequencing verses serotyping, relates engineered MHC molecules to naturally occurring ones, connects genetic loci, alleles, protein chains and multi-chain proteins, and establishes evidence codes for MHC restriction. Where available, this work is based on existing ontologies from the OBO foundry.ConclusionsOverall, representing MHC molecules provides a challenging and practically important test case for ontology building, and could serve as an example of how to integrate other ontology building efforts into web resources.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-016-0045-5) contains supplementary material, which is available to authorized users.

show abstract

“…Vita et al [2] directly propose an extension to a previously-proposed Major Histocompatibility Complex (MHC) Restriction ontology called MaHCO [3], constructed with the assistance of ontology design patterns, while Jupp et al [4] introduce a web-based tool to ease authoring of ontologies with built-in support for enforcing precisely such design patterns.…”

Section: Summary Of Selected Papersmentioning

confidence: 99%

Thematic issue of the Second combined Bio-ontologies and Phenotypes Workshop

et al. 2016

View full text Add to dashboard Cite

This special issue covers selected papers from the 18th Bio-Ontologies Special Interest Group meeting and Phenotype Day, which took place at the Intelligent Systems for Molecular Biology (ISMB) conference in Dublin in 2015. The papers presented in this collection range from descriptions of software tools supporting ontology development and annotation of objects with ontology terms, to applications of text mining for structured relation extraction involving diseases and phenotypes, to detailed proposals for new ontologies and mapping of existing ontologies. Together, the papers consider a range of representational issues in bio-ontology development, and demonstrate the applicability of bio-ontologies to support biological and clinical knowledge-based decision making and analysis.The full set of papers in the Thematic Issue is available at http://www.biomedcentral.com/collections/sig.

show abstract

MaHCO: an ontology of the major histocompatibility complex for immunoinformatic applications and text mining

Abstract: The MaHCO Ontology is available via the BioPortal: http://www.bioontology.org/tools/portal/bioportal.html, and at: http://purl.org/stemnet/.

Cited by 8 publications

References 23 publications

Large-scale biomedical concept recognition: an evaluation of current automatic annotators and their parameters

Large-scale biomedical concept recognition: an evaluation of current automatic annotators and their parameters

An ontology for major histocompatibility restriction

Thematic issue of the Second combined Bio-ontologies and Phenotypes Workshop

Contact Info

Product

Resources

About