44 research outputs found
Conversational narrative: a meta-analysis of narrative analysis
textThis dissertation is a meta-analysis of the narrative analysis
methodologies of Labov and Waletzky (1967), Labov (1972, 1997,
2001, 2002), Polanyi (1985) and Ochs and Capps (2001) using data
from the Minnesota Corpus (Barnes, 1984) to test the usefulness of
these methodologies. Conversational narrative was first a subject
of analysis in the late 60's when Labov and Waletzky, working
under the influence of structural linguistics, decided that in order
to better understand narrative, one must understand its most
basic form, which they felt resided in oral versions of personal
experience. Since their groundbreaking 1967 study, the field of
conversational narrative analysis has been dominated by
structural approaches to narrative that seek to define the
structural components of a narrative and formulate an analysis
based on these components. Only recently with the introduction of
Ochs and Capps' methodology in 2001 has an alternative which
values both the context and the interactive nature of narrative and
seeks to describe the co-participant's influences on narrative been
put forth. This meta-analysis suggests that there are positive and
negative qualities to each of the methodologies at issue and that
different methodologies are more or less appropriate for different
types of data. While the structural approaches to conversational
narrative suggested by Labov and Polanyi do not provide an
adequate means to analyze interactive narratives, Ochs and Capps'
methodology requires more extensive ethnographic information
than what were available from the Minnesota corpus data. While
the Ochs and Capps' approach seems overall to be the best suited
for the type of data at issue in the Minnesota corpus, there are also
clear benefits to be derived from applying a more structural
approach. Specifically, an analysis of a narrative's Non-Storyworld
clauses (as defined by Polanyi) seems to provide important
insights. Moreover, these clauses can help the analyst address
how interlocutors make sense of the relevance of narrative in
coversational discourse, something hinted at by both Labov and
Polanyi. I suggest that a combination of elements from both
structural and ethnographic approaches provides a more complete
methodology with which to analyze interactive narrative data.French and Italia
The Human Phenotype Ontology in 2024: phenotypes around the world
The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs
The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species.
In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven\u27t been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics
Alliance of Genome Resources Portal: unified model organism research platform
The Alliance of Genome Resources (Alliance) is a consortium of the major model organism databases and the Gene Ontology that is guided by the vision of facilitating exploration of related genes in human and well-studied model organisms by providing a highly integrated and comprehensive platform that enables researchers to leverage the extensive body of genetic and genomic studies in these organisms. Initiated in 2016, the Alliance is building a central portal (www.alliancegenome.org) for access to data for the primary model organisms along with gene ontology data and human data. All data types represented in the Alliance portal (e.g. genomic data and phenotype descriptions) have common data models and workflows for curation. All data are open and freely available via a variety of mechanisms. Long-term plans for the Alliance project include a focus on coverage of additional model organisms including those without dedicated curation communities, and the inclusion of new data types with a particular focus on providing data and tools for the non-model-organism researcher that support enhanced discovery about human health and disease. Here we review current progress and present immediate plans for this new bioinformatics resource
Alliance of Genome Resources Portal: unified model organism research platform
The Alliance of Genome Resources (Alliance) is a consortium of the major model organism databases and the Gene Ontology that is guided by the vision of facilitating exploration of related genes in human and well-studied model organisms by providing a highly integrated and comprehensive platform that enables researchers to leverage the extensive body of genetic and genomic studies in these organisms. Initiated in 2016, the Alliance is building a central portal (www.alliancegenome.org) for access to data for the primary model organisms along with gene ontology data and human data. All data types represented in the Alliance portal (e.g. genomic data and phenotype descriptions) have common data models and workflows for curation. All data are open and freely available via a variety of mechanisms. Long-term plans for the Alliance project include a focus on coverage of additional model organisms including those without dedicated curation communities, and the inclusion of new data types with a particular focus on providing data and tools for the non-model-organism researcher that support enhanced discovery about human health and disease. Here we review current progress and present immediate plans for this new bioinformatics resource
The Gene Ontology's Reference Genome Project: A Unified Framework for Functional Annotation across Species
The Gene Ontology (GO) is a collaborative effort that provides structured vocabularies for annotating the molecular function, biological role, and cellular location of gene products in a highly systematic way and in a species-neutral manner with the aim of unifying the representation of gene function across different organisms. Each contributing member of the GO Consortium independently associates GO terms to gene products from the organism(s) they are annotating. Here we introduce the Reference Genome project, which brings together those independent efforts into a unified framework based on the evolutionary relationships between genes in these different organisms. The Reference Genome project has two primary goals: to increase the depth and breadth of annotations for genes in each of the organisms in the project, and to create data sets and tools that enable other genome annotation efforts to infer GO annotations for homologous genes in their organisms. In addition, the project has several important incidental benefits, such as increasing annotation consistency across genome databases, and providing important improvements to the GO's logical structure and biological content
The Human Phenotype Ontology in 2024: phenotypes around the world.
The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs
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The Gene Ontology in 2010: extensions and refinements
The Gene Ontology (GO) Consortium (http://www.geneontology.org) (GOC) continues to develop,
maintain and use a set of structured, controlled
vocabularies for the annotation of genes, gene
products and sequences. The GO ontologies
are expanding both in content and in structure.
Several new relationship types have been introduced
and used, along with existing relationships,
to create links between and within the GO domains.
These improve the representation of biology,
facilitate querying, and allow GO developers to systematically
check for and correct inconsistencies
within the GO. Gene product annotation using GO
continues to increase both in the number of total
annotations and in species coverage. GO tools,
such as OBO-Edit, an ontology-editing tool, and
AmiGO, the GOC ontology browser, have seen
major improvements in functionality, speed and
ease of use.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by Oxford University Press. The published article can be found at: http://nar.oxfordjournals.org/