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Research Project: Impact of Maternal Influence and Early Dietary Factors on Child Growth, Development, and Metabolic Health

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Title: Enhancing childhood nutrition data with open biomedical ontologies

item BONA, JONATHAN - University Arkansas For Medical Sciences (UAMS)
item DIEHL, ALEXANDER - University Of Buffalo
item COX, ALEXANDER - University Of Buffalo
item KEMP, AARON - University Arkansas For Medical Sciences (UAMS)
item LARSON-PRIOR, LINDA - University Arkansas For Medical Sciences (UAMS)

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 7/15/2022
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: This poster reports our ongoing work using open biomedical ontologies to organize and analyze a diverse longitudinal dataset with elements pertaining to childhood nutrition and development. This work is part of a larger research project that seeks to advance understanding of dietary influences on psychological and neuropsychophysiological development and function in children. We are working with a diverse set of 124 data elements for 600 patients, including: infant diet groups (breast milk, soy-based formula, milk-based formula), sex, size measurements (weight, length, head circumference) at birth and at regular intervals, maternal and paternal IQ, maternal and paternal socioeconomic status at yearly intervals, and a variety of mental, language, and motor developmental scores at regular intervals. Our analysis of these data aims to identify discrete phenotypic cohorts and predict cognitive outcome measures over time. Exploratory machine learning analyses of the raw data highlighted the need for additional feature engineering to extract meaningful information from the data. Toward this end we have deployed biomedical ontologies and ontology-based semantic representations that capture connections among these data. We construct and use explicit representations of background knowledge from relevant domain ontologies and have developed an application ontology with a small number of terms that do not currently have coverage elsewhere. These include diet specification, a “plan specification for a diet plan that is realized by a planned process that has as its part feeding behavior involving one or more specific foods,” and diet group role which, much like OBI:study group role, inheres in a population and is realized by the implementation of a planned process following a specification. Developmental measures in our data are of particular relevance to our project aims. The Arkansas neuroinformatics group collaborates with the Neuropsychological Testing Ontology (NPT) group on related projects. Based on our preliminary modeling of these data, we will formally define and request terms to be added to NPT for an array of childhood tests that appear in our dataset and currently lack representation in suitable ontologies. These include: Preschool Language Scales (PLS) receptive and expressive language scores; Bayley Scales Of Infant and Toddler Development (BSID) mental and motor developmental scores; Reynolds Intellectual Assessment Scales (RIAS) verbal, non-verbal, and composite scores; and Symptom Assessment-45 Questionnaire: Depression. We have converted our raw data into ontology-aligned representations in RDF/OWL using custom-built Python and the RDFLib library, along with the OBO-ROBOT tool to extract modules of terms from used ontologies. We store these enhanced data in a triple store for reasoning and query. In addition to providing its own inference mechanisms for reasoning about the data and exposing new connections, the semantic enhancement of these data will be used in feature engineering and selection, in support of further analysis using machine learning methods.