Automatic concept recognition using the Human Phenotype Ontology reference and test suite corpora

Groza, Tudor; Köhler, Sebastian; Doelken, Sandra C.; Collier, Nigel; Oellrich, Anika; Smedley, Damian; Couto, Francisco M.; Baynam, Gareth; Zankl, Andreas; Robinson, Peter N.

doi:10.1093/database/bav005

Cited by 59 publications

(53 citation statements)

References 24 publications

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“…With recent advances in personalized medicine, it is becoming increasingly important to provide a computational foundation for phenotype-driven analysis of genomes and other translational research in other fields of medicine. Consequently, we have extended our work to common human disease phenotypes by means of a text-mining approach (9) toward analyzing the 2014 PubMed corpus, which allowed us to infer 132 620 HPO annotations for 3145 common diseases (10). These annotations were validated against a manually curated subset of disorders and experimental results showed an overall precision of 67%.…”

Section: Hpo: New Terms Annotations and Ontology Integrationmentioning

confidence: 99%

The Human Phenotype Ontology in 2017

et al. 2016

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Deep phenotyping has been defined as the precise and comprehensive analysis of phenotypic abnormalities in which the individual components of the phenotype are observed and described. The three components of the Human Phenotype Ontology (HPO; www.human-phenotype-ontology.org) project are the phenotype vocabulary, disease-phenotype annotations and the algorithms that operate on these. These components are being used for computational deep phenotyping and precision medicine as well as integration of clinical data into translational research. The HPO is being increasingly adopted as a standard for phenotypic abnormalities by diverse groups such as international rare disease organizations, registries, clinical labs, biomedical resources, and clinical software tools and will thereby contribute toward nascent efforts at global data exchange for identifying disease etiologies. This update article reviews the progress of the HPO project since the debut Nucleic Acids Research database article in 2014, including specific areas of expansion such as common (complex) disease, new algorithms for phenotype driven genomic discovery and diagnostics, integration of cross-species mapping efforts with the Mammalian Phenotype Ontology, an improved quality control pipeline, and the addition of patient-friendly terminology.

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Section: Hpo: New Terms Annotations and Ontology Integrationmentioning

confidence: 99%

The Human Phenotype Ontology in 2017

et al. 2016

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“…Negation detection for HPO terms is provided by the NegEx algorithm [28]. An evaluation of the system over a Pubmed corpus is described in [29]. Here, Biolark achieved an F1 score of 0.95 over a test set of 1 933 instances, corresponding to 460 unique HPO concepts.…”

Section: Biomedical Entity Extraction Bio-yodie and Biolarkmentioning

confidence: 99%

CogStack - Experiences Of Deploying Integrated Information Retrieval And Extraction Services In A Large National Health Service Foundation Trust Hospital

Jackson

Kartoglu

Agrawal

et al. 2017

Preprint

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Background: Traditional health information systems are generally devised to support clinical data collection at the point of care. However, as the significance of the modern information economy expands in scope and permeates the healthcare domain, there is an increasing urgency for healthcare organisations to offer information systems that address the expectations of clinicians, researchers and the business intelligence community alike. Amongst other emergent requirements, the principal unmet need might be defined as the 3R principle (right data, right place, right time) to address deficiencies in organisational data flow while retaining the strict information governance policies that apply within the UK National Health Service (NHS). Here, we describe our work on creating and deploying a low cost structured and unstructured information retrieval and extraction architecture within King's College Hospital, the management of governance concerns and the associated use cases and cost saving opportunities that such components present. Results: To date, our CogStack architecture has processed over 300 million lines of clinical data, making it available for internal service improvement projects at King's College London. On generated data designed to simulate real world clinical text, our de-identification algorithm achieved up to 94% precision and up to 96% recall. Conclusion:We describe a toolkit which we feel is of huge value to the UK (and beyond) healthcare community. It is the only open source, easily deployable solution designed for the UK healthcare environment, in a landscape populated by expensive proprietary systems. Solutions such as these provide a crucial foundation for the genomic revolution in medicine.

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“…For curation of ontology-based genotype–phenotype associations (including disease-phenotypic profiles), we are transitioning to the WebPhenote platform (http://create.monarchinitiative.org), which allows a variety of disease entities to be connected to phenotypic descriptors. We also make use of text mining to create seed disease-phenotype associations using the Bio-Lark toolkit (39), which are then manually curated. Most recently, we have performed a large-scale annotation of PubMed to extract common disease-phenotype associations (40).…”

Section: Introductionmentioning

confidence: 99%

The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species

et al. 2016

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The correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype–phenotype associations. Non-human organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype–phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species.

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Automatic concept recognition using the Human Phenotype Ontology reference and test suite corpora

Cited by 59 publications

References 24 publications

The Human Phenotype Ontology in 2017

The Human Phenotype Ontology in 2017

CogStack - Experiences Of Deploying Integrated Information Retrieval And Extraction Services In A Large National Health Service Foundation Trust Hospital

The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species

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