The Experts below are selected from a list of 1602 Experts worldwide ranked by ideXlab platform
Peter N Robinson - One of the best experts on this subject based on the ideXlab platform.
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MedInfo - Integrating the Human Phenotype Ontology into HeTOP Terminology-Ontology Server
Studies in health technology and informatics, 2020Co-Authors: Julien Grosjean, Peter N Robinson, Tayeb Merabti, Lina Fatima Soualmia, Catherine Letord, Jean Charlet, Stefan Jacques DarmoniAbstract:and objective The Human Phenotype Ontology (HPO) is a controlled vocabulary which provides Phenotype data related to genes or diseases. The Health Terminology/Ontology Portal (HeTOP) is a tool dedicated to both Human beings and computers to access and browse biomedical terminologies or ontologies (T/O). The objective of this work was to integrate the HPO into HeTOP in order to enhance both works. This integration is a success and allows users to search and browse the HPO with a dedicated interface. Furthermore, the HPO has been enhanced with the addition of content such as new synonyms, translations, mappings. Integrating T/O such as the HPO into HeTOP is a benefit to vocabularies because it allows enrichment of them and it is also a benefit for HeTOP which provides a better service to both Humans and machines.
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CIBB - Ensembling Descendant Term Classifiers to Improve Gene : Abnormal Phenotype Predictions
Computational Intelligence Methods for Bioinformatics and Biostatistics, 2019Co-Authors: Marco Notaro, Peter N Robinson, Max Schubach, Marco Frasca, Marco Mesiti, Giorgio ValentiniAbstract:The Human Phenotype Ontology (HPO) provides a standard categorization of the phenotypic abnormalities encountered in Human diseases and of the semantic relationship between them. Quite surprisingly the problem of the automated prediction of the association between genes and abnormal Human Phenotypes has been widely overlooked, even if this issue represents an important step toward the characterization of gene-disease associations, especially when no or very limited knowledge is available about the genetic etiology of the disease under study. We present a novel ensemble method able to capture the hierarchical relationships between HPO terms, and able to improve existing hierarchical ensemble algorithms by explicitly considering the predictions of the descendant terms of the Ontology. In this way the algorithm exploits the information embedded in the most specific Ontology terms that closely characterize the phenotypic information associated with each Human gene. Genome-wide results obtained by integrating multiple sources of information show the effectiveness of the proposed approach.
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An ontological foundation for ocular Phenotypes and rare eye diseases.
Orphanet Journal of Rare Diseases, 2019Co-Authors: Panagiotis I. Sergouniotis, Peter N Robinson, Emmanuel Maxime, Dorothée Leroux, Annie Olry, Rachel Thompson, Ana Rath, Hélène DollfusAbstract:Background The optical accessibility of the eye and technological advances in ophthalmic diagnostics have put ophthalmology at the forefront of data-driven medicine. The focus of this study is rare eye disorders, a group of conditions whose clinical heterogeneity and geographic dispersion make data-driven, evidence-based practice particularly challenging. Inter-institutional collaboration and information sharing is crucial but the lack of standardised terminology poses an important barrier. Ontologies are computational tools that include sets of vocabulary terms arranged in hierarchical structures. They can be used to provide robust terminology standards and to enhance data interoperability. Here, we discuss the development of the ophthalmology-related component of two well-established biomedical ontologies, the Human Phenotype Ontology (HPO; includes signs, symptoms and investigation findings) and the Orphanet Rare Disease Ontology (ORDO; includes rare disease nomenclature/nosology).
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Representing glycoPhenotypes: semantic unification of glycobiology resources for disease discovery
Database, 2019Co-Authors: Jean-philippe F. Gourdine, Sebastian Kohler, Nicole Vasilevsky, Julie A Mcmurry, Nicolas Matentzoglu, Xingmin Aaron Zhang, Matthew Brush, Kent Shefchek, Monica C Munoz-torres, Peter N RobinsonAbstract:While abnormalities related to carbohydrates (glycans) are frequent for patients with rare and undiagnosed diseases as well as in many common diseases, these glycan-related Phenotypes (glycoPhenotypes) are not well represented in knowledge bases (KBs). If glycan related diseases were more robustly represented and curated with glycoPhenotypes, these could be used for molecular phenotyping to help realize the goals of precision medicine. Diagnosis of rare diseases by computational cross-species comparison of genotype-Phenotype data has been facilitated by leveraging ontological representations of clinical Phenotypes, using Human Phenotype Ontology (HPO), and model organism ontologies such as Mammalian Phenotype Ontology (MP) in the context of the Monarch Initiative. In this article, we discuss the importance and complexity of glycobiology, and review the structure of glycan-related content from existing KBs and biological ontologies. We show how semantically structuring knowledge about annotation of glycoPhenotypes could enhance disease diagnosis, and propose a solution to integrate glycoPhenotypes and related diseases into the Unified Phenotype Ontology (uPheno), HPO, Monarch, and other KBs. We encourage the community to practice good identifier hygiene for glycans in support of semantic analysis, and clinicians to add glycomics to their diagnostic analyses of rare diseases.
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Evaluation of exome filtering techniques for the analysis of clinically relevant genes.
Human Mutation, 2017Co-Authors: Kristin D. Kernohan, Peter N Robinson, Taila Hartley, Najmeh Alirezaie, David A. Dyment, Kym M. BoycottAbstract:A significant challenge facing clinical translation of exome sequencing is meaningful and efficient variant interpretation. Each exome contains ∼500 rare coding variants; laboratories must systematically and efficiently identify which variant(s) contribute to the patient's Phenotype. In silico filtering is an approach that reduces analysis time while decreasing the chances of incidental findings. We retrospectively assessed 55 solved exomes using available datasets as in silico filters: Online Mendelian Inheritance in Man (OMIM), Orphanet, Human Phenotype Ontology (HPO), and Radboudumc University Medical Center curated panels. We found that personalized panels produced using HPO terms for each patient had the highest success rate (100%), while producing considerably less variants to assess. HPO panels also captured multiple diagnoses in the same individual. We conclude that custom HPO-derived panels are an efficient and effective way to identify clinically relevant exome variants.
Sebastian Kohler - One of the best experts on this subject based on the ideXlab platform.
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Encoding Clinical Data with the Human Phenotype Ontology for Computational Differential Diagnostics
Current protocols in human genetics, 2019Co-Authors: Sebastian Kohler, Nicole Vasilevsky, Tudor Groza, N. Christine Øien, Orion J. Buske, Julius O.b. Jacobsen, Craig Mcnamara, Leigh C. Carmody, J. P. Gourdine, Michael GarganoAbstract:The Human Phenotype Ontology (HPO) is a standardized set of phenotypic terms that are organized in a hierarchical fashion. It is a widely used resource for capturing Human disease Phenotypes for computational analysis to support differential diagnostics. The HPO is frequently used to create a set of terms that accurately describe the observed clinical abnormalities of an individual being evaluated for suspected rare genetic disease. This profile is compared with computational disease profiles in the HPO database with the aim of identifying genetic diseases with comparable phenotypic profiles. The computational analysis can be coupled with the analysis of whole-exome or whole-genome sequencing data through applications such as Exomiser. This article explains how to choose an optimal set of HPO terms for these cases and enter them with software, such as PhenoTips and PatientArchive, and demonstrates how to use Phenomizer and Exomiser to generate a computational differential diagnosis. © 2019 by John Wiley & Sons, Inc.
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expansion of the Human Phenotype Ontology hpo knowledge base and resources
Nucleic Acids Research, 2019Co-Authors: Sebastian Kohler, Nicole Vasilevsky, Julius O.b. Jacobsen, Leigh C. Carmody, Michael Gargano, Daniel Danis, Jeanphilippe Gourdine, Nomi L Harris, Nicolas Matentzoglu, Julie A McmurryAbstract:The Human Phenotype Ontology (HPO)-a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases-is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO's interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the Ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.Human-Phenotype-Ontology.org) simplifies browsing terms and exploring clinical features, diseases, and Human genes.
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Representing glycoPhenotypes: semantic unification of glycobiology resources for disease discovery
Database, 2019Co-Authors: Jean-philippe F. Gourdine, Sebastian Kohler, Nicole Vasilevsky, Julie A Mcmurry, Nicolas Matentzoglu, Xingmin Aaron Zhang, Matthew Brush, Kent Shefchek, Monica C Munoz-torres, Peter N RobinsonAbstract:While abnormalities related to carbohydrates (glycans) are frequent for patients with rare and undiagnosed diseases as well as in many common diseases, these glycan-related Phenotypes (glycoPhenotypes) are not well represented in knowledge bases (KBs). If glycan related diseases were more robustly represented and curated with glycoPhenotypes, these could be used for molecular phenotyping to help realize the goals of precision medicine. Diagnosis of rare diseases by computational cross-species comparison of genotype-Phenotype data has been facilitated by leveraging ontological representations of clinical Phenotypes, using Human Phenotype Ontology (HPO), and model organism ontologies such as Mammalian Phenotype Ontology (MP) in the context of the Monarch Initiative. In this article, we discuss the importance and complexity of glycobiology, and review the structure of glycan-related content from existing KBs and biological ontologies. We show how semantically structuring knowledge about annotation of glycoPhenotypes could enhance disease diagnosis, and propose a solution to integrate glycoPhenotypes and related diseases into the Unified Phenotype Ontology (uPheno), HPO, Monarch, and other KBs. We encourage the community to practice good identifier hygiene for glycans in support of semantic analysis, and clinicians to add glycomics to their diagnostic analyses of rare diseases.
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Phenotero: Annotate as you write
Clinical Genetics, 2018Co-Authors: Daniela Hombach, Dominik Seelow, Jana Marie Schwarz, Ellen Knierim, Markus Schuelke, Sebastian KohlerAbstract:In clinical genetics, the Human Phenotype Ontology as well as disease ontologies are often used for deep phenotyping of patients and coding of clinical diagnoses. However, assigning Ontology classes to patient descriptions is often disconnected from writing patient reports or manuscripts in word processing software. This additional workload and the requirement to install dedicated software may discourage usage of ontologies for parts of the target audience. Here we present Phenotero, a freely available and simple solution to annotate patient Phenotypes and diseases at the time of writing clinical reports or manuscripts. We adopt Zotero, a citation management software to create a tool which allows to reference classes from ontologies within text at the time of writing. We expect this approach to decrease the additional workload to a minimum while ensuring high quality associations with Ontology classes. Standardized collection of phenotypic information at the time of describing the patient allows for streamlining the clinic workflow and efficient data entry. It will subsequently promote clinical and molecular diagnosis with the ultimate goal of better understanding genetic diseases. Thus, we believe that Phenotero eases the usage of ontologies and controlled vocabularies in the field of clinical genetics.
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the Human Phenotype Ontology in 2017
Nucleic Acids Research, 2017Co-Authors: Sebastian Kohler, Nicole Vasilevsky, Mark Engelstad, Erin D Foster, Julie A Mcmurry, Segolene Ayme, Gareth Baynam, Susan M Bello, Cornelius F BoerkoelAbstract: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.
Giorgio Valentini - One of the best experts on this subject based on the ideXlab platform.
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CIBB - Ensembling Descendant Term Classifiers to Improve Gene : Abnormal Phenotype Predictions
Computational Intelligence Methods for Bioinformatics and Biostatistics, 2019Co-Authors: Marco Notaro, Peter N Robinson, Max Schubach, Marco Frasca, Marco Mesiti, Giorgio ValentiniAbstract:The Human Phenotype Ontology (HPO) provides a standard categorization of the phenotypic abnormalities encountered in Human diseases and of the semantic relationship between them. Quite surprisingly the problem of the automated prediction of the association between genes and abnormal Human Phenotypes has been widely overlooked, even if this issue represents an important step toward the characterization of gene-disease associations, especially when no or very limited knowledge is available about the genetic etiology of the disease under study. We present a novel ensemble method able to capture the hierarchical relationships between HPO terms, and able to improve existing hierarchical ensemble algorithms by explicitly considering the predictions of the descendant terms of the Ontology. In this way the algorithm exploits the information embedded in the most specific Ontology terms that closely characterize the phenotypic information associated with each Human gene. Genome-wide results obtained by integrating multiple sources of information show the effectiveness of the proposed approach.
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prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods
BMC Bioinformatics, 2017Co-Authors: Marco Notaro, Peter N Robinson, Max Schubach, Giorgio ValentiniAbstract:The prediction of Human gene–abnormal Phenotype associations is a fundamental step toward the discovery of novel genes associated with Human disorders, especially when no genes are known to be associated with a specific disease. In this context the Human Phenotype Ontology (HPO) provides a standard categorization of the abnormalities associated with Human diseases. While the problem of the prediction of gene–disease associations has been widely investigated, the related problem of gene–phenotypic feature (i.e., HPO term) associations has been largely overlooked, even if for most Human genes no HPO term associations are known and despite the increasing application of the HPO to relevant medical problems. Moreover most of the methods proposed in literature are not able to capture the hierarchical relationships between HPO terms, thus resulting in inconsistent and relatively inaccurate predictions. We present two hierarchical ensemble methods that we formally prove to provide biologically consistent predictions according to the hierarchical structure of the HPO. The modular structure of the proposed methods, that consists in a “flat” learning first step and a hierarchical combination of the predictions in the second step, allows the predictions of virtually any flat learning method to be enhanced. The experimental results show that hierarchical ensemble methods are able to predict novel associations between genes and abnormal Phenotypes with results that are competitive with state-of-the-art algorithms and with a significant reduction of the computational complexity. Hierarchical ensembles are efficient computational methods that guarantee biologically meaningful predictions that obey the true path rule, and can be used as a tool to improve and make consistent the HPO terms predictions starting from virtually any flat learning method. The implementation of the proposed methods is available as an R package from the CRAN repository.
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additional file 7 of prediction of Human Phenotype Ontology terms by means of hierarchical ensemble methods
2017Co-Authors: Marco Notaro, Peter N Robinson, Max Schubach, Giorgio ValentiniAbstract:HPO Prediction of Newly Annotated Genes: detailed experimental results considering only the best predictions for the newly annotated genes. (PDF 77.6 kb)
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prediction of Human gene Phenotype associations by exploiting the hierarchical structure of the Human Phenotype Ontology
International Conference on Bioinformatics and Biomedical Engineering, 2015Co-Authors: Giorgio Valentini, Peter N Robinson, Sebastian Kohler, Matteo Re, Marco NotaroAbstract:The Human Phenotype Ontology (HPO) provides a conceptualization of Phenotype information and a tool for the computational analysis of Human diseases. It covers a wide range of phenotypic abnormalities encountered in Human diseases and its terms (classes) are structured according to a directed acyclic graph. In this context the prediction of the phenotypic abnormalities associated to Human genes is a key tool to stratify patients into disease subclasses that share a common biological or pathophisiological basis. Methods are being developed to predict the HPO terms that are associated for a given disease or disease gene, but most such methods adopt a simple ”flat” approach, that is they do not take into account the hierarchical relationships of the HPO, thus loosing important a priori information about HPO terms. In this contribution we propose a novel Hierarchical Top-Down (HTD) algorithm that associates a specific learner to each HPO term and then corrects the predictions according to the hierarchical structure of the underlying DAG. Genome-wide experimental results relative to a complex HPO DAG including more than 4000 HPO terms show that the proposed hierarchical-aware approach significantly improves predictions obtained with flat methods, especially in terms of precision/recall results.
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IWBBIO (1) - Prediction of Human Gene - Phenotype Associations by Exploiting the Hierarchical Structure of the Human Phenotype Ontology
Bioinformatics and Biomedical Engineering, 2015Co-Authors: Giorgio Valentini, Sebastian Kohler, Matteo Re, Marco Notaro, Peter N RobinsonAbstract:The Human Phenotype Ontology (HPO) provides a conceptualization of Phenotype information and a tool for the computational analysis of Human diseases. It covers a wide range of phenotypic abnormalities encountered in Human diseases and its terms (classes) are structured according to a directed acyclic graph. In this context the prediction of the phenotypic abnormalities associated to Human genes is a key tool to stratify patients into disease subclasses that share a common biological or pathophisiological basis. Methods are being developed to predict the HPO terms that are associated for a given disease or disease gene, but most such methods adopt a simple ”flat” approach, that is they do not take into account the hierarchical relationships of the HPO, thus loosing important a priori information about HPO terms. In this contribution we propose a novel Hierarchical Top-Down (HTD) algorithm that associates a specific learner to each HPO term and then corrects the predictions according to the hierarchical structure of the underlying DAG. Genome-wide experimental results relative to a complex HPO DAG including more than 4000 HPO terms show that the proposed hierarchical-aware approach significantly improves predictions obtained with flat methods, especially in terms of precision/recall results.
Nicole Vasilevsky - One of the best experts on this subject based on the ideXlab platform.
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Encoding Clinical Data with the Human Phenotype Ontology for Computational Differential Diagnostics
Current protocols in human genetics, 2019Co-Authors: Sebastian Kohler, Nicole Vasilevsky, Tudor Groza, N. Christine Øien, Orion J. Buske, Julius O.b. Jacobsen, Craig Mcnamara, Leigh C. Carmody, J. P. Gourdine, Michael GarganoAbstract:The Human Phenotype Ontology (HPO) is a standardized set of phenotypic terms that are organized in a hierarchical fashion. It is a widely used resource for capturing Human disease Phenotypes for computational analysis to support differential diagnostics. The HPO is frequently used to create a set of terms that accurately describe the observed clinical abnormalities of an individual being evaluated for suspected rare genetic disease. This profile is compared with computational disease profiles in the HPO database with the aim of identifying genetic diseases with comparable phenotypic profiles. The computational analysis can be coupled with the analysis of whole-exome or whole-genome sequencing data through applications such as Exomiser. This article explains how to choose an optimal set of HPO terms for these cases and enter them with software, such as PhenoTips and PatientArchive, and demonstrates how to use Phenomizer and Exomiser to generate a computational differential diagnosis. © 2019 by John Wiley & Sons, Inc.
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Semantic integration of clinical laboratory tests from electronic health records for deep phenotyping and biomarker discovery
NPJ digital medicine, 2019Co-Authors: Xingmin Aaron Zhang, Nicole Vasilevsky, Leigh C. Carmody, J. P. Gourdine, Daniel Danis, Amy Yates, Tiffany J. Callahan, Marcin P. Joachimiak, Vida Ravanmehr, Emily R. PfaffAbstract:Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for Phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2923 commonly used laboratory tests with HPO terms. Using these annotations, our software assesses laboratory test results and converts each result into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows readily available laboratory tests in EHR to be reused for deep phenotyping and exploits the hierarchical structure of HPO to integrate distinct tests that have comparable medical interpretations for association studies.
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Semantic Integration of Clinical Laboratory Tests from Electronic Health Records for Deep Phenotyping and Biomarker Discovery
bioRxiv, 2019Co-Authors: Xingmin Aaron Zhang, Nicole Vasilevsky, Leigh C. Carmody, J. P. Gourdine, Daniel Danis, Amy Yates, Marcin P. Joachimiak, Vida Ravanmehr, Emily R. Pfaff, James ChampionAbstract:Electronic Health Record (EHR) systems typically define laboratory test results using the Laboratory Observation Identifier Names and Codes (LOINC) and can transmit them using Fast Healthcare Interoperability Resource (FHIR) standards. LOINC has not yet been semantically integrated with computational resources for Phenotype analysis. Here, we provide a method for mapping LOINC-encoded laboratory test results transmitted in FHIR standards to the Human Phenotype Ontology (HPO) terms. We annotated the medical implications of 2421 commonly used laboratory tests with HPO terms. Using these annotations, a software assesses laboratory test results and converts each into an HPO term. We validated our approach with EHR data from 15,681 patients with respiratory complaints and identified known biomarkers for asthma. Finally, we provide a freely available SMART on FHIR application that can be used within EHR systems. Our approach allows reusing readily available laboratory tests in EHR for deep phenotyping and using the hierarchical structure of HPO for association studies with medical outcomes and genomics.
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expansion of the Human Phenotype Ontology hpo knowledge base and resources
Nucleic Acids Research, 2019Co-Authors: Sebastian Kohler, Nicole Vasilevsky, Julius O.b. Jacobsen, Leigh C. Carmody, Michael Gargano, Daniel Danis, Jeanphilippe Gourdine, Nomi L Harris, Nicolas Matentzoglu, Julie A McmurryAbstract:The Human Phenotype Ontology (HPO)-a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases-is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO's interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the Ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.Human-Phenotype-Ontology.org) simplifies browsing terms and exploring clinical features, diseases, and Human genes.
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Representing glycoPhenotypes: semantic unification of glycobiology resources for disease discovery
Database, 2019Co-Authors: Jean-philippe F. Gourdine, Sebastian Kohler, Nicole Vasilevsky, Julie A Mcmurry, Nicolas Matentzoglu, Xingmin Aaron Zhang, Matthew Brush, Kent Shefchek, Monica C Munoz-torres, Peter N RobinsonAbstract:While abnormalities related to carbohydrates (glycans) are frequent for patients with rare and undiagnosed diseases as well as in many common diseases, these glycan-related Phenotypes (glycoPhenotypes) are not well represented in knowledge bases (KBs). If glycan related diseases were more robustly represented and curated with glycoPhenotypes, these could be used for molecular phenotyping to help realize the goals of precision medicine. Diagnosis of rare diseases by computational cross-species comparison of genotype-Phenotype data has been facilitated by leveraging ontological representations of clinical Phenotypes, using Human Phenotype Ontology (HPO), and model organism ontologies such as Mammalian Phenotype Ontology (MP) in the context of the Monarch Initiative. In this article, we discuss the importance and complexity of glycobiology, and review the structure of glycan-related content from existing KBs and biological ontologies. We show how semantically structuring knowledge about annotation of glycoPhenotypes could enhance disease diagnosis, and propose a solution to integrate glycoPhenotypes and related diseases into the Unified Phenotype Ontology (uPheno), HPO, Monarch, and other KBs. We encourage the community to practice good identifier hygiene for glycans in support of semantic analysis, and clinicians to add glycomics to their diagnostic analyses of rare diseases.
Liwei Wang - One of the best experts on this subject based on the ideXlab platform.
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MedInfo - Using Human Phenotype Ontology for Phenotypic Analysis of Clinical Notes
Studies in health technology and informatics, 2020Co-Authors: Feichen Shen, Liwei WangAbstract:Phenotypes are defined as observable characteristics of organisms. To facilitate the translation between genotype and Phenotype, Human Phenotype Ontology (HPO) was developed as a semantically computable standardized vocabulary to capture phenotypic abnormalities found in Human. In this study, we investigated the use of HPO to annotate phenotypic information in clinical domain by leveraging a corpus of 12.8 million clinical notes created from 2010 to 2015 for 729 thousand patients at Mayo Clinic Rochester campus.
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MedInfo - Phenotypic Analysis of Clinical Narratives Using Human Phenotype Ontology.
Studies in health technology and informatics, 2020Co-Authors: Feichen Shen, Liwei WangAbstract:Phenotypes are defined as observable characteristics and clinical traits of diseases and organisms. As connectors between medical experimental findings and clinical practices, Phenotypes play vital roles in translational medicine. To facilitate the translation between genotype and Phenotype, Human Phenotype Ontology (HPO) was developed as a semantically computable vocabulary to capture phenotypic abnormalities found in Human diseases discovered through biomedical research. The use of HPO in annotating phenotypic information in clinical practice remains unexplored. In this study, we investigated the use of HPO to annotate phenotypic information in clinical domain by leveraging a corpus of 12.8 million clinical notes created from 2010 to 2015 for 729 thousand patients at Mayo Clinic Rochester campus and assessed the distribution information of HPO terms in the corpus. We also analyzed the distributional difference of HPO terms among demographic groups. We further demonstrated the potential application of the annotated corpus to support knowledge discovery in precision medicine through Wilson's Disease.
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hpo2vec leveraging heterogeneous knowledge resources to enrich node embeddings for the Human Phenotype Ontology
Journal of Biomedical Informatics, 2019Co-Authors: Feichen Shen, Yanshan Wang, Suyuan Peng, Liwei WangAbstract:Abstract Background In precision medicine, deep phenotyping is defined as the precise and comprehensive analysis of phenotypic abnormalities, aiming to acquire a better understanding of the natural history of a disease and its genotype-Phenotype associations. Detecting phenotypic relevance is an important task when translating precision medicine into clinical practice, especially for patient stratification tasks based on deep phenotyping. In our previous work, we developed node embeddings for the Human Phenotype Ontology (HPO) to assist in phenotypic relevance measurement incorporating distributed semantic representations. However, the derived HPO embeddings hold only distributed representations for IS-A relationships among nodes, hampering the ability to fully explore the graph. Methods In this study, we developed a framework, HPO2Vec+, to enrich the produced HPO embeddings with heterogeneous knowledge resources (i.e., DECIPHER, OMIM, and Orphanet) for detecting phenotypic relevance. Specifically, we parsed disease-Phenotype associations contained in these three resources to enrich non-inheritance relationships among phenotypic nodes in the HPO. To generate node embeddings for the HPO, node2vec was applied to perform node sampling on the enriched HPO graphs based on random walk followed by feature learning over the sampled nodes to generate enriched node embeddings. Four HPO embeddings were generated based on different graph structures, which we hereafter label as HPOEmb-Original, HPOEmb-DECIPHER, HPOEmb-OMIM, and HPOEmb-Orphanet. We evaluated the derived embeddings quantitatively through an HPO link prediction task with four edge embeddings operations and six machine learning algorithms. The resulting best embeddings were then evaluated for patient stratification of 10 rare diseases using electronic health records (EHR) collected at Mayo Clinic. We assessed our framework qualitatively by visualizing phenotypic clusters and conducting a use case study on primary hyperoxaluria (PH), a rare disease, on the task of inferring relevant Phenotypes given 22 annotated PH related Phenotypes. Results The quantitative link prediction task shows that HPOEmb-Orphanet achieved an optimal AUROC of 0.92 and an average precision of 0.94. In addition, HPOEmb-Orphanet achieved an optimal F1 score of 0.86. The quantitative patient similarity measurement task indicates that HPOEmb-Orphanet achieved the highest average detection rate for similar patients over 10 rare diseases and performed better than other similarity measures implemented by an existing tool, HPOSim, especially for pairwise patients with fewer shared common Phenotypes. The qualitative evaluation shows that the enriched HPO embeddings are generally able to detect relationships among nodes with fine granularity and HPOEmb-Orphanet is particularly good at associating Phenotypes across different disease systems. For the use case of detecting relevant phenotypic characterizations for given PH related Phenotypes, HPOEmb-Orphanet outperformed the other three HPO embeddings by achieving the highest average P@5 of 0.81 and the highest P@10 of 0.79. Compared to seven conventional similarity measurements provided by HPOSim, HPOEmb-Orphanet is able to detect more relevant phenotypic pairs, especially for pairs not in inheritance relationships. Conclusion We drew the following conclusions based on the evaluation results. First, with additional non-inheritance edges, enriched HPO embeddings can detect more associations between fine granularity phenotypic nodes regardless of their topological structures in the HPO graph. Second, HPOEmb-Orphanet not only can achieve the optimal performance through link prediction and patient stratification based on phenotypic similarity, but is also able to detect relevant Phenotypes closer to domain expert’s judgments than other embeddings and conventional similarity measurements. Third, incorporating heterogeneous knowledge resources do not necessarily result in better performance for detecting relevant Phenotypes. From a clinical perspective, in our use case study, clinical-oriented knowledge resources (e.g., Orphanet) can achieve better performance in detecting relevant phenotypic characterizations compared to biomedical-oriented knowledge resources (e.g., DECIPHER and OMIM).
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constructing node embeddings for Human Phenotype Ontology to assist phenotypic similarity measurement
IEEE International Conference on Healthcare Informatics, 2018Co-Authors: Feichen Shen, Liwei Wang, Yanshan Wang, Andrew Harold LimperAbstract:The Human Phenotype Ontology (HPO) was developed to be a semantically computable vocabulary that captures the phenotypic abnormalities found in Human diseases discovered through biomedical research. Usage of this Ontology facilitates the translation between genotype and Phenotype. Many studies have been conducted to accelerate the implementation of precision medicine into clinical practice by utilizing the informative contents provided in the HPO. No work, however, has been done in constructing a distributed representation for nodes in HPO to provide a deep insight of phenotypic similarities by analyzing its graph structure. Node2vec is a model for generating node embeddings based on large networks. In this study, we constructed node embeddings for the HPO leveraging node2vec to assist phenotypic similarity measurement. A downstream application on link prediction driven by HPO embedding achieved 0.81 ROAUC and 0.75 F-measure. A use case study was conducted on idiopathic pulmonary fibrosis (IPF) and we demonstrated the potential possibility of using HPO embeddings in assisting phenotypic similarity measurement.
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ICHI Workshops - Constructing Node Embeddings for Human Phenotype Ontology to Assist Phenotypic Similarity Measurement
2018 IEEE International Conference on Healthcare Informatics Workshop (ICHI-W), 2018Co-Authors: Feichen Shen, Liwei Wang, Yanshan Wang, Andrew Harold LimperAbstract:The Human Phenotype Ontology (HPO) was developed to be a semantically computable vocabulary that captures the phenotypic abnormalities found in Human diseases discovered through biomedical research. Usage of this Ontology facilitates the translation between genotype and Phenotype. Many studies have been conducted to accelerate the implementation of precision medicine into clinical practice by utilizing the informative contents provided in the HPO. No work, however, has been done in constructing a distributed representation for nodes in HPO to provide a deep insight of phenotypic similarities by analyzing its graph structure. Node2vec is a model for generating node embeddings based on large networks. In this study, we constructed node embeddings for the HPO leveraging node2vec to assist phenotypic similarity measurement. A downstream application on link prediction driven by HPO embedding achieved 0.81 ROAUC and 0.75 F-measure. A use case study was conducted on idiopathic pulmonary fibrosis (IPF) and we demonstrated the potential possibility of using HPO embeddings in assisting phenotypic similarity measurement.