Ontologies and Knowledge Graphs in Oncology Research

Silva, Marta Contreiras; Eugénio, Patrícia; Faria, Daniel; Pesquita, Cátia

doi:10.3390/cancers14081906

Cited by 16 publications

(6 citation statements)

References 142 publications

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“…In this respect, VIDO and CIDO benefit from alignment with IDO Core, which itself aligns with the Ontology of General Medical Science (OGMS), whose scope extends beyond infectious disease. What this means in practice is that, for example, kidney disease [ 118 ] and cancer [ 119 ] researchers accurately using the OGMS vocabulary to represent data, invariably use ontology terms and methodologies common to IDO Core, VIDO, and CIDO, thereby lowering barriers to data integration and interoperability.…”

Section: Discussionmentioning

confidence: 99%

Coordinating virus research: The Virus Infectious Disease Ontology

Beverley,

Babcock,

Carvalho

et al. 2024

PLoS ONE

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The COVID-19 pandemic prompted immense work on the investigation of the SARS-CoV-2 virus. Rapid, accurate, and consistent interpretation of generated data is thereby of fundamental concern. Ontologies–structured, controlled, vocabularies–are designed to support consistency of interpretation, and thereby to prevent the development of data silos. This paper describes how ontologies are serving this purpose in the COVID-19 research domain, by following principles of the Open Biological and Biomedical Ontology (OBO) Foundry and by reusing existing ontologies such as the Infectious Disease Ontology (IDO) Core, which provides terminological content common to investigations of all infectious diseases. We report here on the development of an IDO extension, the Virus Infectious Disease Ontology (VIDO), a reference ontology covering viral infectious diseases. We motivate term and definition choices, showcase reuse of terms from existing OBO ontologies, illustrate how ontological decisions were motivated by relevant life science research, and connect VIDO to the Coronavirus Infectious Disease Ontology (CIDO). We next use terms from these ontologies to annotate selections from life science research on SARS-CoV-2, highlighting how ontologies employing a common upper-level vocabulary may be seamlessly interwoven. Finally, we outline future work, including bacteria and fungus infectious disease reference ontologies currently under development, then cite uses of VIDO and CIDO in host-pathogen data analytics, electronic health record annotation, and ontology conflict-resolution projects.

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Section: Discussionmentioning

confidence: 99%

Coordinating virus research: The Virus Infectious Disease Ontology

Beverley,

Babcock,

Carvalho

et al. 2024

PLoS ONE

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show abstract

“…Finally, they filtered the results to a final set of 32 articles for the analysis of the results. In this scoping review, famous ontologies in the field of healthcare were discussed, such as the SNOMED-CT ontology (SCTO) [25], Dietary Lifestyle Ontology (DILON) [26], and International Classification of Diseases (ICD) [27], among others. However, the researchers solely concentrated on medical-related aspects and neglected security concerns.…”

Section: Related Workmentioning

confidence: 99%

Developing a Novel Ontology for Cybersecurity in Internet of Medical Things-Enabled Remote Patient Monitoring

Bughio,

Cook,

Shah

2024

Sensors

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IoT has seen remarkable growth, particularly in healthcare, leading to the rise of IoMT. IoMT integrates medical devices for real-time data analysis and transmission but faces challenges in data security and interoperability. This research identifies a significant gap in the existing literature regarding a comprehensive ontology for vulnerabilities in medical IoT devices. This paper proposes a fundamental domain ontology named MIoT (Medical Internet of Things) ontology, focusing on cybersecurity in IoMT (Internet of Medical Things), particularly in remote patient monitoring settings. This research will refer to similar-looking acronyms, IoMT and MIoT ontology. It is important to distinguish between the two. IoMT is a collection of various medical devices and their applications within the research domain. On the other hand, MIoT ontology refers to the proposed ontology that defines various concepts, roles, and individuals. MIoT ontology utilizes the knowledge engineering methodology outlined in Ontology Development 101, along with the structured life cycle, and establishes semantic interoperability among medical devices to secure IoMT assets from vulnerabilities and cyberattacks. By defining key concepts and relationships, it becomes easier to understand and analyze the complex network of information within the IoMT. The MIoT ontology captures essential key terms and security-related entities for future extensions. A conceptual model is derived from the MIoT ontology and validated through a case study. Furthermore, this paper outlines a roadmap for future research, highlighting potential impacts on security automation in healthcare applications.

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“…The table also shows whether or not the constructed graph from each study if available is based on entity linking with DO and SYMP ontologies, and whether or not the graph integrated with the ontologies. Our paper focuses on a cancer case study where some researchers stated the importance of generating knowledge graphs in oncology research [53]. Further research made efforts to build cancer-related knowledge graphs, but they are either building it manually as in the work of [54] or are building it based on genes studies [55] which will be useful for biomedical engineers and medical professionals but not for normal users.…”

Section: Related Workmentioning

confidence: 99%

An Automatic Generation of Heterogeneous Knowledge Graph for Global Disease Support: A Demonstration of a Cancer Use Case

Maghawry

Ghoniemy

Shaaban

et al. 2023

BDCC

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Semantic data integration provides the ability to interrelate and analyze information from multiple heterogeneous resources. With the growing complexity of medical ontologies and the big data generated from different resources, there is a need for integrating medical ontologies and finding relationships between distinct concepts from different ontologies where these concepts have logical medical relationships. Standardized Medical Ontologies are explicit specifications of shared conceptualization, which provide predefined medical vocabulary that serves as a stable conceptual interface to medical data sources. Intelligent Healthcare systems such as disease prediction systems require a reliable knowledge base that is based on Standardized medical ontologies. Knowledge graphs have emerged as a powerful dynamic representation of a knowledge base. In this paper, a framework is proposed for automatic knowledge graph generation integrating two medical standardized ontologies- Human Disease Ontology (DO), and Symptom Ontology (SYMP) using a medical online website and encyclopedia. The framework and methodologies adopted for automatically generating this knowledge graph fully integrated the two standardized ontologies. The graph is dynamic, scalable, easily reproducible, reliable, and practically efficient. A subgraph for cancer terms is also extracted and studied for modeling and representing cancer diseases, their symptoms, prevention, and risk factors.

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Ontologies and Knowledge Graphs in Oncology Research

Cited by 16 publications

References 142 publications

Coordinating virus research: The Virus Infectious Disease Ontology

Coordinating virus research: The Virus Infectious Disease Ontology

Developing a Novel Ontology for Cybersecurity in Internet of Medical Things-Enabled Remote Patient Monitoring

An Automatic Generation of Heterogeneous Knowledge Graph for Global Disease Support: A Demonstration of a Cancer Use Case

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