PMO: A knowledge representation model towards precision medicine

Hou, Li; Wu, Meng; Kang, Hong; Zheng, S. Q.; Shen, Lingyu; Qian, Qing; Jiao, Li

doi:10.3934/mbe.2020227

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…Hou et al [ 10 ] propose a large and open knowledge representation model dedicated to precision medicine, the Precision Medicine Ontology (PMO). They developed a semi-automated method, from the collection of terms (4.53 million terms, from 62 biomedical vocabularies) to the evaluation and applications of PMO.…”

Section: Resultsmentioning

confidence: 99%

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Knowledge Representation and Management: Interest in New Solutions for Ontology Curation

Dhombres

Charlet

2021

Yearb Med Inform

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Summary Objective: To select, present and summarize some of the best papers in the field of Knowledge Representation and Management (KRM) published in 2020. Methods: A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers of KRM published in 2020, based on PubMed queries. This review was conducted according to the IMIA Yearbook guidelines. Results: Four best papers were selected among 1,175 publications. In contrast with the papers selected last year, the four best papers of 2020 demonstrated a significant focus on methods and tools for ontology curation and design. The usual KRM application domains (bioinformatics, machine learning, and electronic health records) were also represented. Conclusion: In 2020, ontology curation emerges as a significant topic of research interest. Bioinformatics, machine learning, and electronics health records remain significant research areas in the KRM community with various applications. Knowledge representations are key to advance machine learning by providing context and to develop novel bioinformatics metrics. As in 2019, representations serve a great variety of applications across many medical domains, with actionable results and now with growing adhesion to the open science initiative.

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Section: Resultsmentioning

confidence: 99%

“…The final step of the selection of papers was achieved during a meeting of the whole editorial board, based on the reviews and the report of the section editors. [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Five reviewers reviewed these pre-selected papers to select the best four final papers [5][6][7][8].…”

Section: Paper Selection Methodsmentioning

confidence: 99%

Knowledge Representation and Management: Interest in New Solutions for Ontology Curation

Dhombres

Charlet

2021

Yearb Med Inform

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The Immunopeptidomics Ontology (ImPO)

Faria,

Eugénio,

Contreiras Silva

et al. 2024

Database

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The adaptive immune response plays a vital role in eliminating infected and aberrant cells from the body. This process hinges on the presentation of short peptides by major histocompatibility complex Class I molecules on the cell surface. Immunopeptidomics, the study of peptides displayed on cells, delves into the wide variety of these peptides. Understanding the mechanisms behind antigen processing and presentation is crucial for effectively evaluating cancer immunotherapies. As an emerging domain, immunopeptidomics currently lacks standardization—there is neither an established terminology nor formally defined semantics—a critical concern considering the complexity, heterogeneity, and growing volume of data involved in immunopeptidomics studies. Additionally, there is a disconnection between how the proteomics community delivers the information about antigen presentation and its uptake by the clinical genomics community. Considering the significant relevance of immunopeptidomics in cancer, this shortcoming must be addressed to bridge the gap between research and clinical practice. In this work, we detail the development of the ImmunoPeptidomics Ontology, ImPO, the first effort at standardizing the terminology and semantics in the domain. ImPO aims to encapsulate and systematize data generated by immunopeptidomics experimental processes and bioinformatics analysis. ImPO establishes cross-references to 24 relevant ontologies, including the National Cancer Institute Thesaurus, Mondo Disease Ontology, Logical Observation Identifier Names and Codes and Experimental Factor Ontology. Although ImPO was developed using expert knowledge to characterize a large and representative data collection, it may be readily used to encode other datasets within the domain. Ultimately, ImPO facilitates data integration and analysis, enabling querying, inference and knowledge generation and importantly bridging the gap between the clinical proteomics and genomics communities. As the field of immunogenomics uses protein-level immunopeptidomics data, we expect ImPO to play a key role in supporting a rich and standardized description of the large-scale data that emerging high-throughput technologies are expected to bring in the near future. Ontology URL: https://zenodo.org/record/10237571 Project GitHub: https://github.com/liseda-lab/ImPO/blob/main/ImPO.owl

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