Dataset Information

Machine actionable metadata models.

ABSTRACT: Community-developed minimum information checklists are designed to drive the rich and consistent reporting of metadata, underpinning the reproducibility and reuse of the data. These reporting guidelines, however, are usually in the form of narratives intended for human consumption. Modular and reusable machine-readable versions are also needed. Firstly, to provide the necessary quantitative and verifiable measures of the degree to which the metadata descriptors meet these community requirements, a requirement of the FAIR Principles. Secondly, to encourage the creation of standards-driven templates for metadata authoring, especially when describing complex experiments that require multiple reporting guidelines to be used in combination or extended. We present new functionalities to support the creation and improvements of machine-readable models. We apply the approach to an exemplar set of reporting guidelines in Life Science and discuss the challenges. Our work, targeted to developers of standards and those familiar with standards, promotes the concept of compositional metadata elements and encourages the creation of community-standards which are modular and interoperable from the onset.

SUBMITTER: Batista D

PROVIDER: S-EPMC9525592 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Machine actionable metadata models.

Batista Dominique D Gonzalez-Beltran Alejandra A Sansone Susanna-Assunta SA Rocca-Serra Philippe P

Scientific data 20220930 1

Community-developed minimum information checklists are designed to drive the rich and consistent reporting of metadata, underpinning the reproducibility and reuse of the data. These reporting guidelines, however, are usually in the form of narratives intended for human consumption. Modular and reusable machine-readable versions are also needed. Firstly, to provide the necessary quantitative and verifiable measures of the degree to which the metadata descriptors meet these community requirements, ...[more]

PMID: 36180441

Similar Datasets

Project description:Physicians establish diagnosis by assessing a patient's signs, symptoms, age, sex, laboratory test findings and the disease history. All this must be done in limited time and against the backdrop of an increasing overall workload. In the era of evidence-based medicine it is utmost important for a clinician to be abreast of the latest guidelines and treatment protocols which are changing rapidly. In resource limited settings, the updated knowledge often does not reach the point-of-care. This paper presents an artificial intelligence (AI)-based approach for integrating comprehensive disease knowledge, to support physicians and healthcare workers in arriving at accurate diagnoses at the point-of-care. We integrated different disease-related knowledge bodies to construct a comprehensive, machine interpretable diseasomics knowledge-graph that includes the Disease Ontology, disease symptoms, SNOMED CT, DisGeNET, and PharmGKB data. The resulting disease-symptom network comprises knowledge from the Symptom Ontology, electronic health records (EHR), human symptom disease network, Disease Ontology, Wikipedia, PubMed, textbooks, and symptomology knowledge sources with 84.56% accuracy. We also integrated spatial and temporal comorbidity knowledge obtained from EHR for two population data sets from Spain and Sweden respectively. The knowledge graph is stored in a graph database as a digital twin of the disease knowledge. We use node2vec (node embedding) as digital triplet for link prediction in disease-symptom networks to identify missing associations. This diseasomics knowledge graph is expected to democratize the medical knowledge and empower non-specialist health workers to make evidence based informed decisions and help achieve the goal of universal health coverage (UHC). The machine interpretable knowledge graphs presented in this paper are associations between various entities and do not imply causation. Our differential diagnostic tool focusses on signs and symptoms and does not include a complete assessment of patient's lifestyle and health history which would typically be necessary to rule out conditions and to arrive at a final diagnosis. The predicted diseases are ordered according to the specific disease burden in South Asia. The knowledge graphs and the tools presented here can be used as a guide.

Dataset Information

Machine actionable metadata models.

Publications

Machine actionable metadata models.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets