An Ontology to Standardize Research Output of Nutritional Epidemiology: From Paper-Based Standards to Linked Content

Background: The use of linked data in the Semantic Web is a promising approach to add value to nutrition research. An ontology, which defines the logical relationships between well-defined taxonomic terms, enables linking and harmonizing research output. To enable the description of domain-specific output in nutritional epidemiology, we propose the Ontology for Nutritional Epidemiology (ONE) according to authoritative guidance for nutritional epidemiology. Methods: Firstly, a scoping review was conducted to identify existing ontology terms for reuse in ONE. Secondly, existing data standards and reporting guidelines for nutritional epidemiology were converted into an ontology. The terms used in the standards were summarized and listed separately in a taxonomic hierarchy. Thirdly, the ontologies of the nutritional epidemiologic standards, reporting guidelines, and the core concepts were gathered in ONE. Three case studies were included to illustrate potential applications: (i) annotation of existing manuscripts and data, (ii) ontology-based inference, and (iii) estimation of reporting completeness in a sample of nine manuscripts. Results: Ontologies for “food and nutrition” (n = 37), “disease and specific population” (n = 100), “data description” (n = 21), “research description” (n = 35), and “supplementary (meta) data description” (n = 44) were reviewed and listed. ONE consists of 339 classes: 79 new classes to describe data and 24 new classes to describe the content of manuscripts. Conclusion: ONE is a resource to automate data integration, searching, and browsing, and can be used to assess reporting completeness in nutritional epidemiology.

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Perspective: Towards Automated Tracking of Content and Evidence Appraisal of Nutrition Research

Advances in Nutrition ◽

10.1093/advances/nmaa057 ◽

2020 ◽

Vol 11 (5) ◽

pp. 1079-1088

Author(s):

Chen Yang ◽

Dana Hawwash ◽

Bernard De Baets ◽

Jildau Bouwman ◽

Carl Lachat

Keyword(s):

Language Processing ◽

Linked Data ◽

Reporting Guidelines ◽

Training Data ◽

Nutritional Epidemiology ◽

Graph Database ◽

Automated Tracking ◽

Nutrition Research ◽

Healthy Diets ◽

Study Characteristics

ABSTRACT Robust recommendations for healthy diets and nutrition require careful synthesis of available evidence. Given the increasing volume of research articles generated, the retrieval and synthesis of evidence are increasingly becoming laborious and time-consuming. Information technology could help to reduce workload for humans. To guide supervised learning however, human identification of key study characteristics is necessary. Reporting guidelines recommend that authors include essential content in articles and could generate manually labeled training data for automated evidence retrieval and synthesis. Here, we present a semiautomated approach to annotate, link, and track the content of nutrition research manuscripts. We used the STROBE extension for nutritional epidemiology (STROBE-nut) reporting guidelines to manually annotate a sample of 15 articles and converted the semantic information into linked data in a Neo4j graph database through an automated process. Six summary statistics were computed to estimate the reporting completeness of the articles. The content structure, presence of essential study characteristics as well as the reporting completeness of the articles are visualized automatically from the graph database. The archived linked data are interoperable through their annotations and relations. A graph database with linked data on essential study characteristics can enable Natural Language Processing in nutrition.

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Omics-Driven Novel Technologies in Food and Nutrition Research

Nutrigenomics and Nutraceuticals ◽

10.1201/9781315153711-2 ◽

2017 ◽

pp. 19-60

Author(s):

Rakesh Kumar Tekade ◽

Namrata Soni ◽

Soni Neetu ◽

Rahul Maheshwari ◽

Nidhi Raval ◽

...

Keyword(s):

Novel Technologies ◽

Nutrition Research ◽

Food And Nutrition

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Advances in Food and Nutrition Research

Journal of Nutrition Education and Behavior ◽

10.1016/j.jneb.2017.01.006 ◽

2017 ◽

Vol 49 (4) ◽

pp. 362 ◽

Cited By ~ 1

Author(s):

Carol A. Friesen

Keyword(s):

Nutrition Research ◽

Food And Nutrition

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Archaeological Food and Nutrition Research:

Research Methods for Anthropological Studies of Food and Nutrition ◽

10.2307/j.ctvw049xx.14 ◽

2017 ◽

pp. 145-151

Author(s):

Patti J. Wright

Keyword(s):

Nutrition Research ◽

Food And Nutrition

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Domain Specific Categories and Relations and their Potential Applications: A Case Study of Two Arrays of Agriculture Schedule of Colon Classification

10.5771/9783956504402-171 ◽

2012 ◽

pp. 171-175

Author(s):

A. Y. Asundi

Keyword(s):

Domain Specific ◽

Potential Applications

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FOBI: an ontology to represent food intake data and associate it with metabolomic data

Database ◽

10.1093/databa/baaa033 ◽

2020 ◽

Vol 2020 ◽

Cited By ~ 2

Author(s):

Pol Castellano-Escuder ◽

Raúl González-Domínguez ◽

David S Wishart ◽

Cristina Andrés-Lacueva ◽

Alex Sánchez-Pla

Keyword(s):

Food Intake ◽

Enrichment Analysis ◽

Metabolite Identification ◽

Data Availability ◽

Ontology Language ◽

Nutrition Research ◽

Open Biomedical Ontologies ◽

Potential Applications ◽

Raw Foods ◽

Nutritional Data

Abstract Nutrition research can be conducted by using two complementary approaches: (i) traditional self-reporting methods or (ii) via metabolomics techniques to analyze food intake biomarkers in biofluids. However, the complexity and heterogeneity of these two very different types of data often hinder their analysis and integration. To manage this challenge, we have developed a novel ontology that describes food and their associated metabolite entities in a hierarchical way. This ontology uses a formal naming system, category definitions, properties and relations between both types of data. The ontology presented is called FOBI (Food-Biomarker Ontology) and it is composed of two interconnected sub-ontologies. One is a ’Food Ontology’ consisting of raw foods and ‘multi-component foods’ while the second is a ‘Biomarker Ontology’ containing food intake biomarkers classified by their chemical classes. These two sub-ontologies are conceptually independent but interconnected by different properties. This allows data and information regarding foods and food biomarkers to be visualized in a bidirectional way, going from metabolomics to nutritional data or vice versa. Potential applications of this ontology include the annotation of foods and biomarkers using a well-defined and consistent nomenclature, the standardized reporting of metabolomics workflows (e.g. metabolite identification, experimental design) or the application of different enrichment analysis approaches to analyze nutrimetabolomic data. Availability: FOBI is freely available in both OWL (Web Ontology Language) and OBO (Open Biomedical Ontologies) formats at the project’s Github repository (https://github.com/pcastellanoescuder/FoodBiomarkerOntology) and FOBI visualization tool is available in https://polcastellano.shinyapps.io/FOBI_Visualization_Tool/.

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Hierarchical Profiling, Scoring and Applications in Bioinformatics

Advanced Data Mining Technologies in Bioinformatics ◽

10.4018/978-1-59140-863-5.ch002 ◽

2011 ◽

pp. 13-31

Author(s):

Li Liao

Keyword(s):

Biological Data ◽

Classification Methods ◽

Homology Detection ◽

Domain Specific ◽

Wide Range ◽

Recent Developments ◽

Potential Applications ◽

Domain Specific Knowledge ◽

Clustering And Classification ◽

Biological Entities

Recently, clustering and classification methods have seen many applications in bioinformatics. Some are simply straightforward applications of existing techniques, but most have been adapted to cope with peculiar features of the biological data. Many biological data take a form of vectors, whose components correspond to attributes characterizing the biological entities being studied. Comparing these vectors, aka profiles, are a crucial step for most clustering and classification methods. We review the recent developments related to hierarchical profiling where the attributes are not independent, but rather are correlated in a hierarchy. Hierarchical profiling arises in a wide range of bioinformatics problems, including protein homology detection, protein family classification, and metabolic pathway clustering. We discuss in detail several clustering and classification methods where hierarchical correlations are tackled in effective and efficient ways, by incorporation of domain-specific knowledge. Relations to other statistical learning methods and more potential applications are also discussed.

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Food synergy: the key to a healthy diet

Proceedings of The Nutrition Society ◽

10.1017/s0029665112003011 ◽

2013 ◽

Vol 72 (2) ◽

pp. 200-206 ◽

Cited By ~ 77

Author(s):

David R. Jacobs ◽

Linda C. Tapsell

Keyword(s):

Chronic Disease ◽

Nutrient Deficiency ◽

Nutritional Epidemiology ◽

Research Strategies ◽

Diet Patterns ◽

Nutrition Science ◽

Nutrition Research ◽

New Thinking ◽

Diet Pattern ◽

Highly Correlated

Food synergy is the concept that the non-random mixture of food constituents operates in concert for the life of the organism eaten and presumably for the life of the eater. Isolated nutrients have been extensively studied in well-designed, long-term, large randomised clinical trials, typically with null and sometimes with harmful effects. Therefore, although nutrient deficiency is a known phenomenon, serious for the sufferer, and curable by taking the isolated nutrient, the effect of isolated nutrients or other chemicals derived from food on chronic disease, when that chemical is not deficient, may not have the same beneficial effect. It appears that the focus on nutrients rather than foods is in many ways counterproductive. This observation is the basis for the argument that nutrition research should focus more strongly on foods and on dietary patterns. Unlike many dietary phenomena in nutritional epidemiology, diet pattern appears to be highly correlated over time within person. A consistent and robust conclusion is that certain types of beneficial diet patterns, notably described with words such as ‘Mediterranean’ and ‘prudent’, or adverse patterns, often described by the word ‘Western’, predict chronic disease. Food is much more complex than drugs, but essentially uninvestigated as food or pattern. The concept of food synergy leads to new thinking in nutrition science and can help to forge rational nutrition policy-making and to determine future nutrition research strategies.

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