The Clinical Bioinformatics Ontology: A Curated Semantic Network Utilizing Refseq Information

Hoffman, Mark A.; Arnoldi, C.; Chuang, I-Hsun

doi:10.1142/9789812702456_0014

Cited by 19 publications

(17 citation statements)

References 18 publications

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“…The Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) has a strong representation of clinical conditions, The Logical Observation Identifiers Names and Codes (LO-INC) provides reasonable coverage of possible genetic test procedures and the Clinical Bioinformatics Ontology (CBO) provides a semantically structured vocabulary representing the physical observations of the diagnostic laboratory (variants observed, cytogenetic findings etc.) (Hoffman et al 2005). For example, a clinician might order a cystic fibrosis screening panel that is associated with a LOINC code.…”

Section: Codification Of Genetic Test Resultsmentioning

confidence: 99%

Electronic medical records and personalized medicine

Hoffman

Williams

2011

Hum Genet

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If the dream of personalized medicine is to be realized, tremendous amounts of data specific to each individual must be captured, synthesized and presented to clinicians at the time this information is needed to make care decisions for the patient. This can only be accomplished through the use of sophisticated electronic medical record (EMR) systems that are designed to support this function. This article will define two important aspects of a fully functional EMR the ability to: present patients or clinicians with high quality context specific information at the point of care (so-called "just-in time" education) and to combine clinically relevant information from disparate sources in order to guide the clinician to the optimized intervention for a given patient (clinical decision support). Personalized medicine examples are used to illustrate these concepts. As implemented most EMR systems are not being used to assimilate the information needed to provide personalized medicine. A description of necessary enhancements to currently available systems that will be needed to create a "personalized medicine enabled" EMR is provided.

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Section: Codification Of Genetic Test Resultsmentioning

confidence: 99%

Electronic medical records and personalized medicine

Hoffman

Williams

2011

Hum Genet

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“…In a second study [31] , Hoffman and colleagues provided clinical terminology that covered genetic concepts for use in a hospital information system (HIS). In our current research, the genetics knowledge and health knowledge were represented using the terminology of a knowledge base (OntoKBCF) which, in turn, was used for clinical decision support to actively and dynamically filter the knowledge facts displayed to the user.…”

Section: Discussionmentioning

confidence: 99%

Incorporating personalized gene sequence variants, molecular genetics knowledge, and health knowledge into an EHR prototype based on the Continuity of Care Record standard

Jing

Kay

Marley

et al. 2012

Journal of Biomedical Informatics

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Summary Objectives The current volume and complexity of genetic tests, and the molecular genetics knowledge and health knowledge related to interpretation of the results of those tests, are rapidly outstripping the ability of individual clinicians to recall, understand and convey to their patients information relevant to their care. The tailoring of molecular genetics knowledge and health knowledge in clinical settings is important both for the provision of personalized medicine and to reduce clinician information overload. In this paper we describe the incorporation, customization and demonstration of molecular genetic data (mainly sequence variants), molecular genetics knowledge and health knowledge into a standards-based electronic health record (EHR) prototype developed specifically for this study. Methods We extended the CCR (Continuity of Care Record), an existing EHR standard for representing clinical data, to include molecular genetic data. An EHR prototype was built based on the extended CCR and designed to display relevant molecular genetics knowledge and health knowledge from an existing knowledge base for cystic fibrosis (OntoKBCF). We reconstructed test records from published case reports and represented them in the CCR schema. We then used the EHR to dynamically filter molecular genetics knowledge and health knowledge from OntoKBCF using molecular genetic data and clinical data from the test cases. Results The molecular genetic data were successfully incorporated in the CCR by creating a category of laboratory results called “Molecular Genetics ” and specifying a particular class of test (“Gene Mutation Test”) in this category. Unlike other laboratory tests reported in the CCR, results of tests in this class required additional attributes (“Molecular Structure” and “Molecular Position”) to support interpretation by clinicians. These results, along with clinical data (age, sex, ethnicity, diagnostic procedures, and therapies) were used by the EHR to filter and present molecular genetics knowledge and health knowledge from OntoKBCF. Conclusions This research shows a feasible model for delivering patient sequence variants and presenting tailored molecular genetics knowledge and health knowledge via a standards-based EHR system prototype. EHR standards can be extended to include the necessary patient data (as we have demonstrated in the case of the CCR), while knowledge can be obtained from external knowledge bases that are created and maintained independently from the EHR. This approach can form the basis for a personalized medicine framework, a more comprehensive standards-based EHR system and a potential platform for advancing translational research by both disseminating results and providing opportunities for new insights into phenotype-genotype relationships.

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“…Bioinformatics search and software tools have been developed to define the characteristics which distinguish allergens from nonallergens (Zhang et al 2006;Power et al 2013). This kind of categorization helps to organize allergenic proteins so that they can be better with further research in facilitating specific immunotherapies that could possibly be used to help allergic individuals to develop immunological tolerance to peanuts (Stadler and Stadler 2003;Hoffman et al 2005).…”

Section: Allerdb Database and Integrated Bioinformatics Toolsmentioning

confidence: 99%

Peanut Bioinformatics: Tools and Applications for Developing More Effective Immunotherapies for Peanut Allergy and Improving Food Safety

Kandula

Gottschalk

Katam

et al. 2014

Agricultural Bioinformatics

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Advanced tools of bioinformatics have been employed to assess the features critically required for allergenicity and cross-reactivity. A tremendous accumulation of data on plant proteins in recent years has made it possible to classify allergens in different protein families, with most food allergens grouped into four protein families. These families can be grouped together into superfamilies by comparing sequences and related structures. This information makes it possible to identify a wide range of related proteins that may result in the development of multiple food allergies that initiate the development of cross-reactive antibodies in susceptible individuals. Since peanut allergies are responsible for most episodes of food-induced anaphylaxis, a detailed immunological and molecular characterization of these allergenic components is essential to develop suitable immunotherapies. This would also allow us to screen transgenic plants for the possible development of allergens similar to those allergenic components in peanuts. Homology modeling in combination with residue-wise solvent accessibility of monomers and biological assemblies of allergens certainly gives valuable information about antigenic determinants on protein allergens. Through this review, we discuss the applications of bioinformatics tools toward the mitigation of peanut allergy.

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The Clinical Bioinformatics Ontology: A Curated Semantic Network Utilizing Refseq Information

Cited by 19 publications

References 18 publications

Electronic medical records and personalized medicine

Electronic medical records and personalized medicine

Incorporating personalized gene sequence variants, molecular genetics knowledge, and health knowledge into an EHR prototype based on the Continuity of Care Record standard

Peanut Bioinformatics: Tools and Applications for Developing More Effective Immunotherapies for Peanut Allergy and Improving Food Safety

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