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The Sickle Cell Disease Ontology: enabling universal sickle cell-based knowledge representation.

ABSTRACT: Sickle cell disease (SCD) is one of the most common monogenic diseases in humans with multiple phenotypic expressions that can manifest as both acute and chronic complications. Although described more than a century ago, challenges in comprehensive disease management and collaborative research on this disease are compounded by the complex molecular and clinical phenotypes of SCD, environmental and psychosocial factors, limited therapeutic options and ambiguous terminology. This ambiguous terminology has hampered the integration and interoperability of existing SCD knowledge, and SCD research translation. The SCD Ontology (SCDO), which is a community-driven integrative and universal knowledge representation system for SCD, overcomes this issue by providing a controlled vocabulary developed by a group of experts in both SCD and ontology design. SCDO is the first and most comprehensive standardized human- and machine-readable resource that unambiguously represents terminology and concepts about SCD for researchers, patients and clinicians. It is built around the central concept 'hemoglobinopathy', allowing inclusion of non-SCD haemoglobinopathies, such as thalassaemias, which may interfere with or influence SCD phenotypic manifestations. This collaboratively developed ontology constitutes a comprehensive knowledge management system and standardized terminology of various SCD-related factors. The SCDO will promote interoperability of different research datasets, facilitate seamless data sharing and collaborations, including meta-analyses within the SCD community, and support the development and curation of data-basing and clinical informatics in SCD.

SUBMITTER: Sickle Cell Disease Ontology Working Group

PROVIDER: S-EPMC6878945 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Advancements in treatment have contributed to increased survivorship among children with sickle cell disease (SCD). Increased transition readiness, encompassing disease knowledge and self-management skills before transfer to adult care, is necessary to ensure optimal health outcomes. The Sickle Cell Transition E-Learning Program (STEP) is a public, Web-based, 6-module tool designed to increase transition readiness for youth with SCD. OBJECTIVE:The objective of our study was to investigate the participation rate of youth with SCD in STEP and its association with transition readiness. METHODS:This was a single-center, Institution Review Board-approved, retrospective cohort review. A total of 183 youths with SCD, aged between 12 and 15 years, were offered STEP as an adjunct to in-clinic disease education sessions. Participation rate (number of patients who used at least one STEP module divided by those approached) was calculated. The association among the number of STEP modules completed, disease knowledge, and self-management was explored. RESULTS:Overall, 53 of the 183 approached adolescents completed at least one STEP module, yielding a participation rate in STEP of 29.0%. Of the 53 participants, 37 and 39 adolescents had disease knowledge and self-management confidence rating available, respectively. A positive correlation (r=0.47) was found between the number of STEP modules completed and disease knowledge scores (P=.003). No association was found between the number of modules completed and self-management confidence ratings. Disease knowledge scores were significantly higher among participants who completed ?3 STEP modules compared with those who completed <3 STEP modules (U=149.00; P=.007). CONCLUSIONS:Improvement in disease knowledge in adolescence is critical to ensure the youth's ability to self-care during the period of transition to adult care. Despite low participation, the cumulative exposure to the STEP program suggested greater promotion of disease knowledge among adolescents with SCD before transfer to adult care.

Project description:Rheumatism represents any disease condition marked with inflammation and pain in the joints, muscles, or connective tissues. Many traditional Chinese drugs have been used for a long time to treat rheumatism. However, a comprehensive information source for these drugs is still missing, and their anti-rheumatism mechanisms remain unclear. An ontology for anti-rheumatism traditional Chinese drugs would strongly support the representation, analysis, and understanding of these drugs.In this study, we first systematically collected reported information about 26 traditional Chinese decoction pieces drugs, including their chemical ingredients and adverse events (AEs). By mostly reusing terms from existing ontologies (e.g., TCMDPO for traditional Chinese medicines, NCBITaxon for taxonomy, ChEBI for chemical elements, and OAE for adverse events) and making semantic axioms linking different entities, we developed the Ontology of Chinese Medicine for Rheumatism (OCMR) that includes over 3000 class terms. Our OCMR analysis found that these 26 traditional Chinese decoction pieces are made from anatomic entities (e.g., root and stem) from 3 Bilateria animals and 23 Mesangiospermae plants. Anti-inflammatory and antineoplastic roles are important for anti-rheumatism drugs. Using the total of 555 unique ChEBI chemical entities identified from these drugs, our ChEBI-based classification analysis identified 18 anti-inflammatory, 33 antineoplastic chemicals, and 9 chemicals (including 3 diterpenoids and 3 triterpenoids) having both anti-inflammatory and antineoplastic roles. Furthermore, our study detected 22 diterpenoids and 23 triterpenoids, including 16 pentacyclic triterpenoids that are likely bioactive against rheumatism. Six drugs were found to be associated with 184 unique AEs, including three AEs (i.e., dizziness, nausea and vomiting, and anorexia) each associated with 5 drugs. Several chemical entities are classified as neurotoxins (e.g., diethyl phthalate) and allergens (e.g., eugenol), which may explain the formation of some TCD AEs. The OCMR could be efficiently queried for useful information using SPARQL scripts.The OCMR ontology was developed to systematically represent 26 traditional anti-rheumatism Chinese drugs and their related information. The OCMR analysis identified possible anti-rheumatism and AE mechanisms of these drugs. Our novel ontology-based approach can also be applied to systematic representation and analysis of other traditional Chinese drugs.

Project description:MotivationBiological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena within the domain. The structure and information contained in ontologies and their annotations make them valuable for developing machine learning, data analysis and knowledge extraction algorithms; notably, semantic similarity is widely used to identify relations between biological entities, and ontology-based annotations are frequently used as features in machine learning applications.ResultsWe propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering. To evaluate Onto2Vec, we use the gene ontology (GO) and jointly produce dense vector representations of proteins, the GO classes to which they are annotated, and the axioms in GO that constrain these classes. First, we demonstrate that Onto2Vec-generated feature vectors can significantly improve prediction of protein-protein interactions in human and yeast. We then illustrate how Onto2Vec representations provide the means for constructing data-driven, trainable semantic similarity measures that can be used to identify particular relations between proteins. Finally, we use an unsupervised clustering approach to identify protein families based on their Enzyme Commission numbers. Our results demonstrate that Onto2Vec can generate high quality feature vectors from biological entities and ontologies. Onto2Vec has the potential to significantly outperform the state-of-the-art in several predictive applications in which ontologies are involved.Availability and implementationhttps://github.com/bio-ontology-research-group/onto2vec.Supplementary informationSupplementary data are available at Bioinformatics online.

Project description:OBJECTIVES:To assess current knowledge of National Heart, Lung and Blood Institutes (NHLBI) and Thalassemia International Federation (TIF) recommendations, blood banking practices and perceived challenges among transfusion services in the management of patients with haemoglobinopathies. BACKGROUND:Previous reports have demonstrated variations in transfusion practices for sickle cell disease (SCD) and thalassemia patients. Recently, NHLBI/TIF have provided transfusion recommendations for patients with haemoglobinopathies. METHODS:A cross-sectional survey was conducted of transfusion services from the state of Georgia previously identified as having SCD/thalassemia populations. The survey assessed transfusion service practices in pre-transfusion testing and blood product selection; awareness/implementation of NHLBI/TIF transfusion-based recommendations and perceived challenges in transfusing haemoglobinopathy patients. RESULTS:Responses were received from 35 of 49 (71%) institutions. Only institutions indicating transfusing SCD or thalassemia patients (32) were included in analysis. Seventy-one percent of non-sickle cell treatment centres (SCTCs) and 20% of non-thalassemia treatment centres follow NHLBI and TIF recommendations to perform a red blood cell phenotype beyond ABO/Rh(D) and provide Rh and Kell prophylactically matched units for SCD and thalassemia patients, respectively. Forty percent of institutions (33% of non-SCTCs) employ RBC genotyping to evaluate the red cell phenotype for SCD patients. Over 77% of institutions do not utilise a reliable method to identify SCD patients prior to transfusion, such as a required question/answer field on type/screen or crossmatch orders. CONCLUSION:Many healthcare systems' transfusion practices for haemoglobinopathy patients are discordant with NHLBI/TIF recommendations. Efforts are needed to increase awareness and implementation of current recommendations among all transfusion services seeing these patients.

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The Sickle Cell Disease Ontology: enabling universal sickle cell-based knowledge representation.

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