ONSTR: The ontology for newborn screening follow-up and translational research

Snezana Nikolic, Prabhu Rv Shankar, Sivaram Arabandi, Akshaye Dhawan, Rajshekhar Sunderraman, Sham Navathe, Kunal Malhotra, Rani H. Singh

Research output: Contribution to journalArticlepeer-review

Abstract

Translational research in the field of newborn screening system requires integration of data generated during various phases of life long treatment of patients identified and diagnosed through newborn dried blood spot screening (NDBS). In this paper, we describe the Ontology for Newborn Screening Follow-up and Translational Research (ONSTR). ONSTR is an application ontology for representing data entities, practices and knowledge in the domain of newborn screening short- and long-term follow-up of patients diagnosed with inheritable and congenital disorders. It will serve as a core of the data integration framework in the Newborn Screening Follow-up Data Integration Collaborative (NBSDC), designed to support Semantic Web tools and applications with the goal of helping clinicians involved in translational research. Here, we describe the ONSTR domain, our top-down bottom-up methodological approach to ontology modelling using phenylketonuria (PKU) as an exemplar, and some of the lessons learned. We provide an illustration of our ontological model of three important aspects of PKU: 1) the etiology, 2) the phenylalanine hydroxylase enzyme dysfunction underlying PKU and 3) the disambiguation of terms central to PKU appearing in the literature. In modelling of the mechanism of phenylalanine hydroxylase enzyme (PAH enzyme) dysfunction, we encountered limitations in using Gene Onotology (GO) process classes, in terms of their over-granularity and the lack of representations of process participants. As a solution to this problem and in order to accurately represent this process, we created a hybrid model of enzyme mediated biochemical reactions. This model of PKU and enzymatic reactions will serve as a prototype for modelling other inherited metabolic disorders (IMDs) and enzymatic processes of importance to clinical and translational research in the NBDS long-term follow-up domain. This initial work provides the ontological foundation for automated reasoning, integration and annotation of data collected through the newborn screening system. Copyright

Original languageEnglish (US)
Pages (from-to)54-61
Number of pages8
JournalCEUR Workshop Proceedings
Volume1060
StatePublished - 2013
Externally publishedYes

ASJC Scopus subject areas

  • Computer Science(all)

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