Persona:
Martínez Tomás, Rafael

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ORCID
0000-0003-4706-7168
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Martínez Tomás
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Rafael
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2019 - 201912020 - 20233
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Autor: Rincón Zamorano, Mariano × Fecha de inicio: 2010 ×

Resultados de la búsqueda

Mostrando 1 - 4 de 4
  • Miniatura
    Publicación
    An overview of graph databases and their applications in the biomedical domain
    (Oxford University Press, 2021-05-18) Timón Reina, Santiago; Rincón Zamorano, Mariano; Martínez Tomás, Rafael
    Over the past couple of decades, the explosion of densely interconnected data has stimulated the research, development and adoption of graph database technologies. From early graph models to more recent native graph databases, the landscape of implementations has evolved to cover enterprise-ready requirements. Because of the interconnected nature of its data, the biomedical domain has been one of the early adopters of graph databases, enabling more natural representation models and better data integration workflows, exploration and analysis facilities. In this work, we survey the literature to explore the evolution, performance and how the most recent graph database solutions are applied in the biomedical domain, compiling a great variety of use cases. With this evidence, we conclude that the available graph database management systems are fit to support data-intensive, integrative applications, targeted at both basic research and exploratory tasks closer to the clinic.
  • Miniatura
    Publicación
    Ontologies for early detection of the Alzheimer Disease and other Neurodegenerative Diseases
    (Springer, 2019) Gómez-Valades Batanero, Alba; Martínez Tomás, Rafael; Rincón Zamorano, Mariano
    Nowadays technologies allow an exponential generation of biomedical data, which must be indexed according to some standard criteria to be useful to the scientific and medical community, being neurology one of the areas in which the standardization is more necessary. Ontologies have been highlighted as one of the best options, with their capability of homogenise information, allowing their integration with other kind of information, and the inference of new information based on the data that is stored. We analyse and compare the approaches taken by different research groups inside the area of the Alzheimer’s disease, and the ontologies they developed with the objective of providing a common framework to standardize information, data recovery or as a part of an expert system. However, to make this approach work the ontologies must be maintained over the time, a critical point which is not been followed by any of the ontologies reviewed.
  • Miniatura
    Publicación
    Integrative Base Ontology for the Research Analysis of Alzheimer’s Disease-Related Mild Cognitive Impairment
    (Frontiers, 2021-02-04) Gómez-Valades Batanero, Alba; Martínez Tomás, Rafael; Rincón Zamorano, Mariano
    Early detection of mild cognitive impairment (MCI) has become a priority in Alzheimer’s disease (AD) research, as it is a transitional phase between normal aging and dementia. However, information on MCI and AD is scattered across different formats and standards generated by different technologies, making it difficult to work with them manually. Ontologies have emerged as a solution to this problem due to their capacity for homogenization and consensus in the representation and reuse of data. In this context, an ontology that integrates the four main domains of neurodegenerative diseases, diagnostic tests, cognitive functions, and brain areas will be of great use in research. Here, we introduce the first approach to this ontology, the Neurocognitive Integrated Ontology (NIO), which integrates the knowledge regarding neuropsychological tests (NT), AD, cognitive functions, and brain areas. This ontology enables interoperability and facilitates access to data by integrating dispersed knowledge across different disciplines, rendering it useful for other research groups. To ensure the stability and reusability of NIO, the ontology was developed following the ontology-building life cycle, integrating and expanding terms from four different reference ontologies. The usefulness of this ontology was validated through use-case scenarios.
  • Miniatura
    Publicación
    A Knowledge Graph Framework for Dementia Research Data
    (MDPI, 2023-09-20) Timón Reina, Santiago; Kirsebom, Bjørn-Eivind; Fladby, Tormod; Rincón Zamorano, Mariano; Martínez Tomás, Rafael
    Dementia disease research encompasses diverse data modalities, including advanced imaging, deep phenotyping, and multi-omics analysis. However, integrating these disparate data sources has historically posed a significant challenge, obstructing the unification and comprehensive analysis of collected information. In recent years, knowledge graphs have emerged as a powerful tool to address such integration issues by enabling the consolidation of heterogeneous data sources into a structured, interconnected network of knowledge. In this context, we introduce DemKG, an open-source framework designed to facilitate the construction of a knowledge graph integrating dementia research data, comprising three core components: a KG-builder that integrates diverse domain ontologies and data annotations, an extensions ontology providing necessary terms tailored for dementia research, and a versatile transformation module for incorporating study data. In contrast with other current solutions, our framework provides a stable foundation by leveraging established ontologies and community standards and simplifies study data integration while delivering solid ontology design patterns, broadening its usability. Furthermore, the modular approach of its components enhances flexibility and scalability. We showcase how DemKG might aid and improve multi-modal data investigations through a series of proof-of-concept scenarios focused on relevant Alzheimer’s disease biomarkers.