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An automated identification and analysis of ontological terms in gastrointestinal diseases and nutrition-related literature provides useful insights.

ABSTRACT: With an unprecedented growth in the biomedical literature, keeping up to date with the new developments presents an immense challenge. Publications are often studied in isolation of the established literature, with interpretation being subjective and often introducing human bias. With ontology-driven annotation of biomedical data gaining popularity in recent years and online databases offering metatags with rich textual information, it is now possible to automatically text-mine ontological terms and complement the laborious task of manual management, interpretation, and analysis of the accumulated literature with downstream statistical analysis. In this paper, we have formulated an automated workflow through which we have identified ontological information, including nutrition-related terms in PubMed abstracts (from 1991 to 2016) for two main types of Inflammatory Bowel Diseases: Crohn's Disease and Ulcerative Colitis; and two other gastrointestinal (GI) diseases, namely, Coeliac Disease and Irritable Bowel Syndrome. Our analysis reveals unique clustering patterns as well as spatial and temporal trends inherent to the considered GI diseases in terms of literature that has been accumulated so far. Although automated interpretation cannot replace human judgement, the developed workflow shows promising results and can be a useful tool in systematic literature reviews. The workflow is available at https://github.com/KociOrges/pytag.

SUBMITTER: Koci O

PROVIDER: S-EPMC6064635 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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An automated identification and analysis of ontological terms in gastrointestinal diseases and nutrition-related literature provides useful insights.

Koci Orges O Logan Michael M Svolos Vaios V Russell Richard K RK Gerasimidis Konstantinos K Ijaz Umer Zeeshan UZ

PeerJ 20180726

With an unprecedented growth in the biomedical literature, keeping up to date with the new developments presents an immense challenge. Publications are often studied in isolation of the established literature, with interpretation being subjective and often introducing human bias. With ontology-driven annotation of biomedical data gaining popularity in recent years and online databases offering metatags with rich textual information, it is now possible to automatically text-mine ontological terms ...[more]

PMID: 30065857

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Project description:BackgroundSelf-Medication (SM), i.e. using medications to treat oneself, is a major concern for health researchers and policy makers. The terms "self medication" or "self-medication" (SM terms) have been used to explain various concepts while several terms have also been employed to define this practice. Hence, retrieving relevant publications would require exhaustive literature screening. So, we assessed the current situation of SM terms in the literature to improve the relevancy of search outcomes.MethodsIn this Systematic exploration, SM terms were searched in the 6 following databases and publisher's portals till April 2012: Web of Science, Scopus, PubMed, Google scholar, ScienceDirect, and Wiley. A simple search query was used to include only publications with SM terms. We used Relative-Risk (RR) to estimate the probability of SM terms use in related compared to unrelated publications. Sensitivity and specificity of SM terms as keywords in search query were also calculated. Relevant terms to SM practice were extracted and their Likelihood Ratio positive and negative (LR+/-) were calculated to assess their effect on the probability of search outcomes relevancy in addition to previous search queries. We also evaluated the content of unrelated publications. All mentioned steps were performed in title (TI) and title or abstract (TIAB) of publications.Results1999 related and 1917 unrelated publications were found. SM terms RR was 4.5 in TI and 2.1 in TIAB. SM terms sensitivity and specificity respectively were 55.4% and 87.7% in TI and 84.0% and 59.5% in TIAB. "OTC" and "Over-The-Counter Medication", with LR+ 16.78 and 16.30 respectively, provided the most conclusive increase in the probability of the relevancy of publications. The most common unrelated SM themes were self-medication hypothesis, drug abuse and Zoopharmacognosy.ConclusionsDue to relatively low specificity or sensitivity of SM terms, relevant terms should be employed in search queries and clear definitions of SM applications should be applied to improve the relevancy of publications.

Project description:BACKGROUND:The optical accessibility of the eye and technological advances in ophthalmic diagnostics have put ophthalmology at the forefront of data-driven medicine. The focus of this study is rare eye disorders, a group of conditions whose clinical heterogeneity and geographic dispersion make data-driven, evidence-based practice particularly challenging. Inter-institutional collaboration and information sharing is crucial but the lack of standardised terminology poses an important barrier. Ontologies are computational tools that include sets of vocabulary terms arranged in hierarchical structures. They can be used to provide robust terminology standards and to enhance data interoperability. Here, we discuss the development of the ophthalmology-related component of two well-established biomedical ontologies, the Human Phenotype Ontology (HPO; includes signs, symptoms and investigation findings) and the Orphanet Rare Disease Ontology (ORDO; includes rare disease nomenclature/nosology). METHODS:A variety of approaches were used including automated matching to existing resources and extensive manual curation. To achieve the latter, a study group including clinicians, patient representatives and ontology developers from 17 countries was formed. A broad range of terms was discussed and validated during a dedicated workshop attended by 60 members of the group. RESULTS:A comprehensive, structured and well-defined set of terms has been agreed on including 1106 terms relating to ocular phenotypes (HPO) and 1202 terms relating to rare eye disease nomenclature (ORDO). These terms and their relevant annotations can be accessed in http://www.human-phenotype-ontology.org/ and http://www.orpha.net/ ; comments, corrections, suggestions and requests for new terms can be made through these websites. This is an ongoing, community-driven endeavour and both HPO and ORDO are regularly updated. CONCLUSIONS:To our knowledge, this is the first effort of such scale to provide terminology standards for the rare eye disease community. We hope that this work will not only improve coding and standardise information exchange in clinical care and research, but also it will catalyse the transition to an evidence-based precision ophthalmology paradigm.

Project description:Genome-wide association studies (GWAS) have identified many common single nucleotide polymorphisms (SNPs) that associate with clinical phenotypes, but these SNPs usually explain just a small part of the heritability and have relatively modest effect sizes. In contrast, SNPs that associate with metabolite levels generally explain a higher percentage of the genetic variation and demonstrate larger effect sizes. Still, the discovery of SNPs associated with metabolite levels is challenging since testing all metabolites measured in typical metabolomics studies with all SNPs comes with a severe multiple testing penalty. We have developed an automated workflow approach that utilizes prior knowledge of biochemical pathways present in databases like KEGG and BioCyc to generate a smaller SNP set relevant to the metabolite. This paper explores the opportunities and challenges in the analysis of GWAS of metabolomic phenotypes and provides novel insights into the genetic basis of metabolic variation through the re-analysis of published GWAS datasets.Re-analysis of the published GWAS dataset from Illig et al. (Nature Genetics, 2010) using a pathway-based workflow (http://www.myexperiment.org/packs/319.html), confirmed previously identified hits and identified a new locus of human metabolic individuality, associating Aldehyde dehydrogenase family1 L1 (ALDH1L1) with serine/glycine ratios in blood. Replication in an independent GWAS dataset of phospholipids (Demirkan et al., PLoS Genetics, 2012) identified two novel loci supported by additional literature evidence: GPAM (Glycerol-3 phosphate acyltransferase) and CBS (Cystathionine beta-synthase). In addition, the workflow approach provided novel insight into the affected pathways and relevance of some of these gene-metabolite pairs in disease development and progression.We demonstrate the utility of automated exploitation of background knowledge present in pathway databases for the analysis of GWAS datasets of metabolomic phenotypes. We report novel loci and potential biochemical mechanisms that contribute to our understanding of the genetic basis of metabolic variation and its relationship to disease development and progression.

Dataset Information

An automated identification and analysis of ontological terms in gastrointestinal diseases and nutrition-related literature provides useful insights.

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An automated identification and analysis of ontological terms in gastrointestinal diseases and nutrition-related literature provides useful insights.

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