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1. Synthetic data: How could it be used for infectious disease research?

Author

Fragkouli, Styliani-Christina, Solanki, Dhwani, Castro, Leyla J, Psomopoulos, Fotis E, Queralt-Rosinach, Núria, Cirillo, Davide, and Crossman, Lisa C

Subjects
Quantitative Biology - Other Quantitative Biology, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

Over the last three to five years, it has become possible to generate machine learning synthetic data for healthcare-related uses. However, concerns have been raised about potential negative factors associated with the possibilities of artificial dataset generation. These include the potential misuse of generative artificial intelligence (AI) in fields such as cybercrime, the use of deepfakes and fake news to deceive or manipulate, and displacement of human jobs across various market sectors. Here, we consider both current and future positive advances and possibilities with synthetic datasets. Synthetic data offers significant benefits, particularly in data privacy, research, in balancing datasets and reducing bias in machine learning models. Generative AI is an artificial intelligence genre capable of creating text, images, video or other data using generative models. The recent explosion of interest in GenAI was heralded by the invention and speedy move to use of large language models (LLM). These computational models are able to achieve general-purpose language generation and other natural language processing tasks and are based on transformer architectures, which made an evolutionary leap from previous neural network architectures. Fuelled by the advent of improved GenAI techniques and wide scale usage, this is surely the time to consider how synthetic data can be used to advance infectious disease research. In this commentary we aim to create an overview of the current and future position of synthetic data in infectious disease research.

Published
2024

3. A Simple Standard for Sharing Ontological Mappings (SSSOM)

Author

Matentzoglu, Nicolas, Balhoff, James P., Bello, Susan M., Bizon, Chris, Brush, Matthew, Callahan, Tiffany J., Chute, Christopher G, Duncan, William D., Evelo, Chris T., Gabriel, Davera, Graybeal, John, Gray, Alasdair, Gyori, Benjamin M., Haendel, Melissa, Harmse, Henriette, Harris, Nomi L., Harrow, Ian, Hegde, Harshad, Hoyt, Amelia L., Hoyt, Charles T., Jiao, Dazhi, Jiménez-Ruiz, Ernesto, Jupp, Simon, Kim, Hyeongsik, Koehler, Sebastian, Liener, Thomas, Long, Qinqin, Malone, James, McLaughlin, James A., McMurry, Julie A., Moxon, Sierra, Munoz-Torres, Monica C., Osumi-Sutherland, David, Overton, James A., Peters, Bjoern, Putman, Tim, Queralt-Rosinach, Núria, Shefchek, Kent, Solbrig, Harold, Thessen, Anne, Tudorache, Tania, Vasilevsky, Nicole, Wagner, Alex H., and Mungall, Christopher J.

Subjects
Computer Science - Databases
Abstract

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Are they associated in some other way? Such relationships between the mapped terms are often not documented, leading to incorrect assumptions and making them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Also, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. The Simple Standard for Sharing Ontological Mappings (SSSOM) addresses these problems by: 1. Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. 2. Defining an easy to use table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data standards. 3. Implementing open and community-driven collaborative workflows designed to evolve the standard continuously to address changing requirements and mapping practices. 4. Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases, and survey some existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable, and Reusable (FAIR). The SSSOM specification is at http://w3id.org/sssom/spec., Comment: Corresponding author: Christopher J. Mungall

Published
2021
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4. The GA4GH Phenopacket schema defines a computable representation of clinical data

Author

Jacobsen, Julius OB, Baudis, Michael, Baynam, Gareth S, Beckmann, Jacques S, Beltran, Sergi, Buske, Orion J, Callahan, Tiffany J, Chute, Christopher G, Courtot, Mélanie, Danis, Daniel, Elemento, Olivier, Essenwanger, Andrea, Freimuth, Robert R, Gargano, Michael A, Groza, Tudor, Hamosh, Ada, Harris, Nomi L, Kaliyaperumal, Rajaram, Lloyd, Kevin C Kent, Khalifa, Aly, Krawitz, Peter M, Köhler, Sebastian, Laraway, Brian J, Lehväslaiho, Heikki, Matalonga, Leslie, McMurry, Julie A, Metke-Jimenez, Alejandro, Mungall, Christopher J, Munoz-Torres, Monica C, Ogishima, Soichi, Papakonstantinou, Anastasios, Piscia, Davide, Pontikos, Nikolas, Queralt-Rosinach, Núria, Roos, Marco, Sass, Julian, Schofield, Paul N, Seelow, Dominik, Siapos, Anastasios, Smedley, Damian, Smith, Lindsay D, Steinhaus, Robin, Sundaramurthi, Jagadish Chandrabose, Swietlik, Emilia M, Thun, Sylvia, Vasilevsky, Nicole A, Wagner, Alex H, Warner, Jeremy L, Weiland, Claus, Haendel, Melissa A, and Robinson, Peter N

Subjects
GAGH Phenopacket Modeling Consortium
Published
2022

5. A Simple Standard for Sharing Ontological Mappings (SSSOM)

Author

Matentzoglu, Nicolas, Balhoff, James P, Bello, Susan M, Bizon, Chris, Brush, Matthew, Callahan, Tiffany J, Chute, Christopher G, Duncan, William D, Evelo, Chris T, Gabriel, Davera, Graybeal, John, Gray, Alasdair, Gyori, Benjamin M, Haendel, Melissa, Harmse, Henriette, Harris, Nomi L, Harrow, Ian, Hegde, Harshad B, Hoyt, Amelia L, Hoyt, Charles T, Jiao, Dazhi, Jiménez-Ruiz, Ernesto, Jupp, Simon, Kim, Hyeongsik, Koehler, Sebastian, Liener, Thomas, Long, Qinqin, Malone, James, McLaughlin, James A, McMurry, Julie A, Moxon, Sierra, Munoz-Torres, Monica C, Osumi-Sutherland, David, Overton, James A, Peters, Bjoern, Putman, Tim, Queralt-Rosinach, Núria, Shefchek, Kent, Solbrig, Harold, Thessen, Anne, Tudorache, Tania, Vasilevsky, Nicole, Wagner, Alex H, and Mungall, Christopher J

Subjects
Data Management and Data Science, Information and Computing Sciences, Data Science, Networking and Information Technology R&D (NITRD), Data Management, Databases, Factual, Metadata, Semantic Web, Workflow, Data Format, Library and Information Studies, Bioinformatics and computational biology, Data management and data science
Abstract

Despite progress in the development of standards for describing and exchanging scientific information, the lack of easy-to-use standards for mapping between different representations of the same or similar objects in different databases poses a major impediment to data integration and interoperability. Mappings often lack the metadata needed to be correctly interpreted and applied. For example, are two terms equivalent or merely related? Are they narrow or broad matches? Or are they associated in some other way? Such relationships between the mapped terms are often not documented, which leads to incorrect assumptions and makes them hard to use in scenarios that require a high degree of precision (such as diagnostics or risk prediction). Furthermore, the lack of descriptions of how mappings were done makes it hard to combine and reconcile mappings, particularly curated and automated ones. We have developed the Simple Standard for Sharing Ontological Mappings (SSSOM) which addresses these problems by: (i) Introducing a machine-readable and extensible vocabulary to describe metadata that makes imprecision, inaccuracy and incompleteness in mappings explicit. (ii) Defining an easy-to-use simple table-based format that can be integrated into existing data science pipelines without the need to parse or query ontologies, and that integrates seamlessly with Linked Data principles. (iii) Implementing open and community-driven collaborative workflows that are designed to evolve the standard continuously to address changing requirements and mapping practices. (iv) Providing reference tools and software libraries for working with the standard. In this paper, we present the SSSOM standard, describe several use cases in detail and survey some of the existing work on standardizing the exchange of mappings, with the goal of making mappings Findable, Accessible, Interoperable and Reusable (FAIR). The SSSOM specification can be found at http://w3id.org/sssom/spec. Database URL: http://w3id.org/sssom/spec.

Published
2022

7. Nanopublications: A Growing Resource of Provenance-Centric Scientific Linked Data

Author

Kuhn, Tobias, Meroño-Peñuela, Albert, Malic, Alexander, Poelen, Jorrit H., Hurlbert, Allen H., Ortiz, Emilio Centeno, Furlong, Laura I., Queralt-Rosinach, Núria, Chichester, Christine, Banda, Juan M., Willighagen, Egon, Ehrhart, Friederike, Evelo, Chris, Malas, Tareq B., and Dumontier, Michel

Subjects
Computer Science - Digital Libraries
Abstract

Nanopublications are a Linked Data format for scholarly data publishing that has received considerable uptake in the last few years. In contrast to the common Linked Data publishing practice, nanopublications work at the granular level of atomic information snippets and provide a consistent container format to attach provenance and metadata at this atomic level. While the nanopublications format is domain-independent, the datasets that have become available in this format are mostly from Life Science domains, including data about diseases, genes, proteins, drugs, biological pathways, and biotic interactions. More than 10 million such nanopublications have been published, which now form a valuable resource for studies on the domain level of the given Life Science domains as well as on the more technical levels of provenance modeling and heterogeneous Linked Data. We provide here an overview of this combined nanopublication dataset, show the results of some overarching analyses, and describe how it can be accessed and queried.

Published
2018

8. BioHackathon 2015: Semantics of data for life sciences and reproducible research

Author

Vos, Rutger A, Katayama, Toshiaki, Mishima, Hiroyuki, Kawano, Shin, Kawashima, Shuichi, Kim, Jin-Dong, Moriya, Yuki, Tokimatsu, Toshiaki, Yamaguchi, Atsuko, Yamamoto, Yasunori, Wu, Hongyan, Amstutz, Peter, Antezana, Erick, Aoki, Nobuyuki P, Arakawa, Kazuharu, Bolleman, Jerven T, Bolton, Evan, Bonnal, Raoul JP, Bono, Hidemasa, Burger, Kees, Chiba, Hirokazu, Cohen, Kevin B, Deutsch, Eric W, Fernández-Breis, Jesualdo T, Fu, Gang, Fujisawa, Takatomo, Fukushima, Atsushi, García, Alexander, Goto, Naohisa, Groza, Tudor, Hercus, Colin, Hoehndorf, Robert, Itaya, Kotone, Juty, Nick, Kawashima, Takeshi, Kim, Jee-Hyub, Kinjo, Akira R, Kotera, Masaaki, Kozaki, Kouji, Kumagai, Sadahiro, Kushida, Tatsuya, Lütteke, Thomas, Matsubara, Masaaki, Miyamoto, Joe, Mohsen, Attayeb, Mori, Hiroshi, Naito, Yuki, Nakazato, Takeru, Nguyen-Xuan, Jeremy, Nishida, Kozo, Nishida, Naoki, Nishide, Hiroyo, Ogishima, Soichi, Ohta, Tazro, Okuda, Shujiro, Paten, Benedict, Perret, Jean-Luc, Prathipati, Philip, Prins, Pjotr, Queralt-Rosinach, Núria, Shinmachi, Daisuke, Suzuki, Shinya, Tabata, Tsuyosi, Takatsuki, Terue, Taylor, Kieron, Thompson, Mark, Uchiyama, Ikuo, Vieira, Bruno, Wei, Chih-Hsuan, Wilkinson, Mark, Yamada, Issaku, Yamanaka, Ryota, Yoshitake, Kazutoshi, Yoshizawa, Akiyasu C, Dumontier, Michel, Kosaki, Kenjiro, and Takagi, Toshihisa

Subjects
Medical Biochemistry and Metabolomics, Biological Sciences, Bioinformatics and Computational Biology, Biomedical and Clinical Sciences, Networking and Information Technology R&D (NITRD), Genetics, Biological Science Disciplines, Computational Biology, Data Mining, Metadata, Reproducibility of Results, Semantic Web, BioHackathon, Bioinformatics, Databases, Linked Open Data, Ontology, Visualization, Web Services, Workflows, Biochemistry and Cell Biology, Clinical Sciences, Oncology and Carcinogenesis
Abstract

We report on the activities of the 2015 edition of the BioHackathon, an annual event that brings together researchers and developers from around the world to develop tools and technologies that promote the reusability of biological data. We discuss issues surrounding the representation, publication, integration, mining and reuse of biological data and metadata across a wide range of biomedical data types of relevance for the life sciences, including chemistry, genotypes and phenotypes, orthology and phylogeny, proteomics, genomics, glycomics, and metabolomics. We describe our progress to address ongoing challenges to the reusability and reproducibility of research results, and identify outstanding issues that continue to impede the progress of bioinformatics research. We share our perspective on the state of the art, continued challenges, and goals for future research and development for the life sciences Semantic Web.

Published
2020

9. A collaborative network analysis for the interpretation of transcriptomics data in Huntington's disease.

Author

Ozisik, Ozan, Kara, Nazli Sila, Abbassi-Daloii, Tooba, Térézol, Morgane, Kuijper, Elsa C., Queralt-Rosinach, Núria, Jacobsen, Annika, Sezerman, Osman Ugur, Roos, Marco, Evelo, Chris T., Baudot, Anaïs, Ehrhart, Friederike, and Mina, Eleni

Abstract

Rare diseases may affect the quality of life of patients and be life-threatening. Therapeutic opportunities are often limited, in part because of the lack of understanding of the molecular mechanisms underlying these diseases. This can be ascribed to the low prevalence of rare diseases and therefore the lower sample sizes available for research. A way to overcome this is to integrate experimental rare disease data with prior knowledge using network-based methods. Taking this one step further, we hypothesized that combining and analyzing the results from multiple network-based methods could provide data-driven hypotheses of pathogenic mechanisms from multiple perspectives. We analyzed a Huntington's disease transcriptomics dataset using six network-based methods in a collaborative way. These methods either inherently reported enriched annotation terms or their results were fed into enrichment analyses. The resulting significantly enriched Reactome pathways were then summarized using the ontological hierarchy which allowed the integration and interpretation of outputs from multiple methods. Among the resulting enriched pathways, there are pathways that have been shown previously to be involved in Huntington's disease and pathways whose direct contribution to disease pathogenesis remains unclear and requires further investigation. In summary, our study shows that collaborative network analysis approaches are well-suited to study rare diseases, as they provide hypotheses for pathogenic mechanisms from multiple perspectives. Applying different methods to the same case study can uncover different disease mechanisms that would not be apparent with the application of a single method. [ABSTRACT FROM AUTHOR]

Published
2025
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10. Reliable Granular References to Changing Linked Data

Author

Kuhn, Tobias, Willighagen, Egon, Evelo, Chris, Queralt-Rosinach, Núria, Centeno, Emilio, and Furlong, Laura I.

Subjects
Computer Science - Digital Libraries
Abstract

Nanopublications are a concept to represent Linked Data in a granular and provenance-aware manner, which has been successfully applied to a number of scientific datasets. We demonstrated in previous work how we can establish reliable and verifiable identifiers for nanopublications and sets thereof. Further adoption of these techniques, however, was probably hindered by the fact that nanopublications can lead to an explosion in the number of triples due to auxiliary information about the structure of each nanopublication and repetitive provenance and metadata. We demonstrate here that this significant overhead disappears once we take the version history of nanopublication datasets into account, calculate incremental updates, and allow users to deal with the specific subsets they need. We show that the total size and overhead of evolving scientific datasets is reduced, and typical subsets that researchers use for their analyses can be referenced and retrieved efficiently with optimized precision, persistence, and reliability., Comment: In Proceedings of the 16th International Semantic Web Conference (ISWC) 2017

Published
2017

12. Semantic modelling of common data elements for rare disease registries, and a prototype workflow for their deployment over registry data

Author

Kaliyaperumal, Rajaram, Wilkinson, Mark D., Moreno, Pablo Alarcón, Benis, Nirupama, Cornet, Ronald, dos Santos Vieira, Bruna, Dumontier, Michel, Bernabé, César Henrique, Jacobsen, Annika, Le Cornec, Clémence M. A., Godoy, Mario Prieto, Queralt-Rosinach, Núria, Schultze Kool, Leo J., Swertz, Morris A., van Damme, Philip, van der Velde, K. Joeri, Lalout, Nawel, Zhang, Shuxin, and Roos, Marco

Published
2022
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13. Neuro-symbolic representation learning on biological knowledge graphs

Author

Alshahrani, Mona, Khan, Mohammed Asif, Maddouri, Omar, Kinjo, Akira R, Queralt-Rosinach, Núria, and Hoehndorf, Robert

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Learning, Quantitative Biology - Molecular Networks
Abstract

Motivation: Biological data and knowledge bases increasingly rely on Semantic Web technologies and the use of knowledge graphs for data integration, retrieval and federated queries. In the past years, feature learning methods that are applicable to graph-structured data are becoming available, but have not yet widely been applied and evaluated on structured biological knowledge. Results: We develop a novel method for feature learning on biological knowledge graphs. Our method combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs. Through the use of symbolic logic, these embeddings contain both explicit and implicit information. We apply these embeddings to the prediction of edges in the knowledge graph representing problems of function prediction, finding candidate genes of diseases, protein-protein interactions, or drug target relations, and demonstrate performance that matches and sometimes outperforms traditional approaches based on manually crafted features. Our method can be applied to any biological knowledge graph, and will thereby open up the increasing amount of Semantic Web based knowledge bases in biology to use in machine learning and data analytics. Availability and Implementation: https://github.com/bio-ontology-research-group/walking-rdf-and-owl Contact: robert.hoehndorf@kaust.edu.sa

Published
2016
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16. Building Expertise on FAIR Through Evolving Bring Your Own Data (BYOD) Workshops: Describing the Data, Software, and Management-focused Approaches and Their Evolution

Author

CMM, CMM Groep Cuppen, Bernabé, César H., Thielemans, Lieze, Kaliyaperumal, Rajaram, Carta, Claudio, Zhang, Shuxin, van Gelder, Celia W.G., Benis, Nirupama, Santos, Luiz Olavo Bonino da Silva, Cornet, Ronald, Vieira, Bruna Dos Santos, Lalout, Nawel, Henriques, Ines, Ballesteros, Alberto Cámara, Burger, Kees, Kersloot, Martijn G., Ehrhart, Friederike, Enckevort, Esther van, Evelo, Chris T., Gray, Alasdair J.G., Hanauer, Marc, Hettne, Kristina, Ligt, Joep de, Pereira, Arnaldo, Queralt-Rosinach, Núria, Schultes, Erik, Taruscio, Domenica, Waagmeester, Andra, Wilkinson, Mark D., Willighagen, Egon L., Jansen, Mascha, Mons, Barend, Roos, Marco, Jacobsen, Annika, CMM, CMM Groep Cuppen, Bernabé, César H., Thielemans, Lieze, Kaliyaperumal, Rajaram, Carta, Claudio, Zhang, Shuxin, van Gelder, Celia W.G., Benis, Nirupama, Santos, Luiz Olavo Bonino da Silva, Cornet, Ronald, Vieira, Bruna Dos Santos, Lalout, Nawel, Henriques, Ines, Ballesteros, Alberto Cámara, Burger, Kees, Kersloot, Martijn G., Ehrhart, Friederike, Enckevort, Esther van, Evelo, Chris T., Gray, Alasdair J.G., Hanauer, Marc, Hettne, Kristina, Ligt, Joep de, Pereira, Arnaldo, Queralt-Rosinach, Núria, Schultes, Erik, Taruscio, Domenica, Waagmeester, Andra, Wilkinson, Mark D., Willighagen, Egon L., Jansen, Mascha, Mons, Barend, Roos, Marco, and Jacobsen, Annika

Published
2024

17. Building expertise on FAIR through evolving Bring Your Own Data (BYOD) workshops: describing the data, software, and management- focused approaches and their evolution

Author

Bernabé, César H., primary, Thielemans, Lieze, additional, Kaliyaperumal, Rajaram, additional, Carta, Claudio, additional, Zhang, Shuxin, additional, van Gelder, Celia W.G., additional, Benis, Nirupama, additional, da Silva Santos, Luiz Olavo Bonino, additional, Cornet, Ronald, additional, Vieira, Bruna dos Santos, additional, Lalout, Nawel, additional, Henriques, Ines, additional, Ballesteros, Alberto Cámara, additional, Burger, Kees, additional, Kersloot, Martijn G., additional, Ehrhart, Friederike, additional, van Enckevort, Esther, additional, Evelo, Chris T., additional, Gray, Alasdair J. G., additional, Hanauer, Marc, additional, Hettne, Kristina, additional, de Ligt, Joep, additional, Pereira, Arnaldo, additional, Queralt-Rosinach, Núria, additional, Schultes, Erik, additional, Taruscio, Domenica, additional, Waagmeester, Andra, additional, Wilkinson, Mark D., additional, Willighagen, Egon L., additional, Jansen, Mascha, additional, Mons, Barend, additional, Roos, Marco, additional, and Jacobsen, Annika, additional

Published
2023
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18. Collaborative network analysis for the interpretation of transcriptomics data in rare diseases, an application to Huntington’s disease

Author

Ozisik, Ozan, primary, Kara, Nazli Sila, additional, Abbassi-Daloii, Tooba, additional, Térézol, Morgane, additional, Queralt-Rosinach, Núria, additional, Jacobsen, Annika, additional, Sezerman, Osman Ugur, additional, Roos, Marco, additional, Evelo, Chris T., additional, Baudot, Anaïs, additional, Ehrhart, Friederike, additional, and Mina, Eleni, additional

Published
2023
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19. Infrastructure for synthetic health data

Author

Queralt-Rosinach, Núria, primary, Alshaikhdeeb, Basel, additional, Bolzani, Luca, additional, Shoaib, Muhammad, additional, Barbero, Marcos Casado, additional, Aguiló-Castillo, Sergi, additional, Cirillo, Davide, additional, Castro, Leyla Jael, additional, Tsueng, Ginger, additional, Kalaš, Matúš, additional, Palmblad, Magnus, additional, Welter, Danielle, additional, Ghosh, Soumyabrata, additional, Satagopam, Venkata, additional, and Sheriff, Rahuman S. Malik, additional

Published
2023
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22. FAIRification and semantic modelling for Duchenne and Becker Muscular Dystrophy rare diseases - Poster

Author

Siminiuc, Sergiu, Roos, Marco, Perdomo Quinteiro, Pablo, Spitali, Pietro, Queralt Rosinach, Núria, and Sakellariou, Paraskevi

Subjects
FAIR data, Becker Muscular Dystrophy, Rare Diseases, BYOD, Duchenne Muscular Dystrophy, Semantic modelling
Abstract

The BIND project is a EU-funded project that attempts to improve the characterisation of brain involvement in Duchenne and Becker Muscular Dystrophies (DMD and BMD). Here, we present our ongoing work on making multimodal data FAIR, the semantic models, and discuss challenges and opportunities based on our FAIRification experience.

Published
2023

23. Structured Review on Huntington's Disease: Generating Hypotheses on Iron Dysregulation - Poster

Author

Roos, Marco, Cremers, Karolis, Mina, Eleni, Wolstencroft, Katy, and Queralt Rosinach, Núria

Subjects
Knowledge graph, Ontologies, Structured Review, Network Analysis, FAIR, Linked Data
Abstract

Here we present a Structured Review on the relationships of iron with Huntington’s disease. Including relationship predictions made by different edge prediction models, the results of the inclusion of the Gene Ontology structure on relationship predictions and experimental data representation within the SR.

Published
2023

24. Updating the CEMO ontology for future epidemiological challenges - Poster

Author

Queralt Rosinach, Núria, Schofield, Paul, Roos, Marco, and Hoehndorf, Robert

Subjects
Epidemiology, COVID-19, Ontologies, FAIR
Abstract

The COVID-19 epidemiology and monitoring ontology (CEMO) is an OWL ontology built during the COVID-19 pandemic for better exchange, integration and reuse of epidemiological information. Here, we present an update of the development of the ontology and future directions in order to make it usable under different scenarios and new challenges.

Published
2023
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25. Mapping OHDSI OMOP Common Data Model and GA4GH Phenopackets for COVID-19 disease epidemics and analytics

Author

Queralt-Rosinach, Núria, primary, Moreno, Pablo Alarcón, additional, Callahan, Tiffany, additional, Delussu, Giovanni, additional, Fraboulet, Charlotte, additional, Jacobsen, Jules, additional, Castro, Leyla Jael, additional, Kaliyaperumal, Rajaram, additional, Kulmanov, Maxat, additional, Robinson, Peter, additional, Satagopam, Venkata, additional, Siapos, Anastasios, additional, Touré, Vasundra, additional, and Welter, Danielle, additional

Published
2022
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28. Semantic modelling of common data elements for rare disease registries, and a prototype workflow for their deployment over registry data

Author

Alarcón-Moreno, Pablo [0000-0001-5974-589X], Dos Santos Vieira, Bruna [0000-0002-7245-9588], Kaliyaperumal, Rajaram, Wilkinson, Mark D., Alarcón-Moreno, Pablo, Benis, Nirupama, Cornet, Ronald, Dos Santos Vieira, Bruna, Michel Dumontier, Michel, Bernabé, César Henrique, Jacobsen, Annika, Le Cornec, Clémence M. A., Prieto Godoy, Mario, Queralt-Rosinach, Núria, Schultze Kool, Leo J., Swertz, Morris A., van Damme, Philip, Van der Velde, K. Joeri, Lalout, Nawel, Zhang, Shuxin, Roos, Marco, Alarcón-Moreno, Pablo [0000-0001-5974-589X], Dos Santos Vieira, Bruna [0000-0002-7245-9588], Kaliyaperumal, Rajaram, Wilkinson, Mark D., Alarcón-Moreno, Pablo, Benis, Nirupama, Cornet, Ronald, Dos Santos Vieira, Bruna, Michel Dumontier, Michel, Bernabé, César Henrique, Jacobsen, Annika, Le Cornec, Clémence M. A., Prieto Godoy, Mario, Queralt-Rosinach, Núria, Schultze Kool, Leo J., Swertz, Morris A., van Damme, Philip, Van der Velde, K. Joeri, Lalout, Nawel, Zhang, Shuxin, and Roos, Marco

Abstract

Background The European Platform on Rare Disease Registration (EU RD Platform) aims to address the fragmentation of European rare disease (RD) patient data, scattered among hundreds of independent and non-coordinating registries, by establishing standards for integration and interoperability. The first practical output of this effort was a set of 16 Common Data Elements (CDEs) that should be implemented by all RD registries. Interoperability, however, requires decisions beyond data elements - including data models, formats, and semantics. Within the European Joint Programme on Rare Diseases (EJP RD), we aim to further the goals of the EU RD Platform by generating reusable RD semantic model templates that follow the FAIR Data Principles. Results Through a team-based iterative approach, we created semantically grounded models to represent each of the CDEs, using the SemanticScience Integrated Ontology as the core framework for representing the entities and their relationships. Within that framework, we mapped the concepts represented in the CDEs, and their possible values, into domain ontologies such as the Orphanet Rare Disease Ontology, Human Phenotype Ontology and National Cancer Institute Thesaurus. Finally, we created an exemplar, reusable ETL pipeline that we will be deploying over these non-coordinating data repositories to assist them in creating model-compliant FAIR data without requiring site-specific coding nor expertise in Linked Data or FAIR. Conclusions Within the EJP RD project, we determined that creating reusable, expert-designed templates reduced or eliminated the requirement for our participating biomedical domain experts and rare disease data hosts to understand OWL semantics. This enabled them to publish highly expressive FAIR data using tools and approaches that were already familiar to them.

Published
2022

31. Additional file 13 of The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery

Author

Dumontier, Michel, Baker, Christopher JO, Baran, Joachim, Callahan, Alison, Chepelev, Leonid, Cruz-Toledo, José, Del Rio, Nicholas R, Duck, Geraint, Furlong, Laura I, Keath, Nichealla, Klassen, Dana, McCusker, Jamie P, Queralt-Rosinach, Núria, Samwald, Matthias, Villanueva-Rosales, Natalia, Wilkinson, Mark D, and Hoehndorf, Robert

Subjects
Data_FILES
Abstract

Authors’ original file for figure 12

Published
2022
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32. Additional file 5 of The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery

Author

Dumontier, Michel, Baker, Christopher JO, Baran, Joachim, Callahan, Alison, Chepelev, Leonid, Cruz-Toledo, José, Del Rio, Nicholas R, Duck, Geraint, Furlong, Laura I, Keath, Nichealla, Klassen, Dana, McCusker, Jamie P, Queralt-Rosinach, Núria, Samwald, Matthias, Villanueva-Rosales, Natalia, Wilkinson, Mark D, and Hoehndorf, Robert

Subjects
Data_FILES
Abstract

Authors’ original file for figure 4

Published
2022
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33. Additional file 6 of The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery

Author

Dumontier, Michel, Baker, Christopher JO, Baran, Joachim, Callahan, Alison, Chepelev, Leonid, Cruz-Toledo, José, Del Rio, Nicholas R, Duck, Geraint, Furlong, Laura I, Keath, Nichealla, Klassen, Dana, McCusker, Jamie P, Queralt-Rosinach, Núria, Samwald, Matthias, Villanueva-Rosales, Natalia, Wilkinson, Mark D, and Hoehndorf, Robert

Subjects
Data_FILES
Abstract

Authors’ original file for figure 5

Published
2022
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34. Additional file 9 of The Semanticscience Integrated Ontology (SIO) for biomedical research and knowledge discovery

Author

Dumontier, Michel, Baker, Christopher JO, Baran, Joachim, Callahan, Alison, Chepelev, Leonid, Cruz-Toledo, José, Del Rio, Nicholas R, Duck, Geraint, Furlong, Laura I, Keath, Nichealla, Klassen, Dana, McCusker, Jamie P, Queralt-Rosinach, Núria, Samwald, Matthias, Villanueva-Rosales, Natalia, Wilkinson, Mark D, and Hoehndorf, Robert

Subjects
Data_FILES
Abstract

Authors’ original file for figure 8

Published
2022
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35. Goal-models to support communication, planning and guiding of FAIRification

Author

Bernabé, César Henrique, Jacobsen, Annika, Queralt Rosinach, Núria, Bonino da Silva Santos, Luiz Olavo, Silva Souza, Vítor E., Mons, Barend, and Roos, Marco

Subjects
FAIR Principles, Ontology, Goal Modelling
Abstract

Implementing the FAIR principles makes data ready for efficient analysis with other data. Workflows for the process of making data FAIR (‘FAIRification’) describe how the principles can be realised. As a multidisciplinary activity, FAIRification relies on good communication with different expertise involved. However, FAIRification workflows currently do not specify methods to meet this need. We are designing a method that uses ‘goal-oriented models’ to support the FAIRification ‘objective identification’ and ‘conceptual modelling’ steps. In the former, the motivation(s) for the need for FAIR data are identified. In the latter, goal models are used to define the scope, identify important concepts and validate the resulting conceptual model. The method will also describe best practices and activities for conceptual modelling.

Published
2021
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36. A Simple Standard for Sharing Ontological Mappings (SSSOM)

Author

Matentzoglu, Nicolas, primary, Balhoff, James P, additional, Bello, Susan M, additional, Bizon, Chris, additional, Brush, Matthew, additional, Callahan, Tiffany J, additional, Chute, Christopher G, additional, Duncan, William D, additional, Evelo, Chris T, additional, Gabriel, Davera, additional, Graybeal, John, additional, Gray, Alasdair, additional, Gyori, Benjamin M, additional, Haendel, Melissa, additional, Harmse, Henriette, additional, Harris, Nomi L, additional, Harrow, Ian, additional, Hegde, Harshad B, additional, Hoyt, Amelia L, additional, Hoyt, Charles T, additional, Jiao, Dazhi, additional, Jiménez-Ruiz, Ernesto, additional, Jupp, Simon, additional, Kim, Hyeongsik, additional, Koehler, Sebastian, additional, Liener, Thomas, additional, Long, Qinqin, additional, Malone, James, additional, McLaughlin, James A, additional, McMurry, Julie A, additional, Moxon, Sierra, additional, Munoz-Torres, Monica C, additional, Osumi-Sutherland, David, additional, Overton, James A, additional, Peters, Bjoern, additional, Putman, Tim, additional, Queralt-Rosinach, Núria, additional, Shefchek, Kent, additional, Solbrig, Harold, additional, Thessen, Anne, additional, Tudorache, Tania, additional, Vasilevsky, Nicole, additional, Wagner, Alex H, additional, and Mungall, Christopher J, additional

Published
2022
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37. Applying the FAIR principles to data in a hospital: an interdisciplinary collaboration

Author

Queralt-Rosinach, Núria

Subjects
FAIR data, Hospital, Open Science, Linked Data, Ontologies, Health Data, patient data, Semantic Web
Abstract

FAIR data principles and open science are globally endorsed as beneficial for healthcare. As co-founders of the FAIR principles we investigate implementation of FAIR principles, particularly interoperability for machines and interdisciplinary FAIRification. The Covid-19 pandemic emphasized that FAIRification ‘at source’ is vital: observational data are first collected in hospitals and should become FAIR for researchers as quickly as possible, inside and outside of the hospital. However, multiple information systems are used inside hospitals that are not directly interoperable. At the same time, existing systems have their own value such that replacing them is not desirable. Here, we present a strategy to implement FAIR principles that complements existing hospital systems. We coordinated the FAIRification of observational data of hospitalised patients within an interdisciplinary collaboration that was organised within the hospital to face the Covid-19 challenges. We defined an architecture around ontological models that link data in existing systems. Guided by research questions of the medical doctors, we transformed data into machine actionable digital objects, and developed ontological models for data and metadata, including investigational parameters. DCAT2-structured metadata was exposed by FAIR Data Points. We demonstrated machine actionability by (i) federated queries across hospital data and existing Linked Data-based knowledge sources, (ii) Web APIs for querying Linked Data, (iii) hypothesis-support applications built on top of FAIR patient data. Our work demonstrates that a FAIR research data management plan based on interdisciplinary collaboration and ontological models for data and metadata, Semantic Web technologies, and FAIR Data Points can complement hospital infrastructure to make machine-actionable FAIR digital objects available for integrative analysis. This prepares hospital systems for federated analysis (e.g. as part of the European Open Science Cloud), linking to other FAIR data such as Linked Open Data, and reuse in software applications.

Published
2021
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38. The GA4GH Phenopacket schema: A computable representation of clinical data for precision medicine

Author

Jacobsen, Julius O. B., primary, Baudis, Michael, additional, Baynam, Gareth S., additional, Beckmann, Jacques S., additional, Beltran, Sergi, additional, Callahan, Tiffany J., additional, Chute, Christopher G., additional, Courtot, Mélanie, additional, Danis, Daniel, additional, Elemento, Olivier, additional, Freimuth, Robert R., additional, Gargano, Michael A., additional, Groza, Tudor, additional, Hamosh, Ada, additional, Harris, Nomi L., additional, Kaliyaperumal, Rajaram, additional, Khalifa, Aly, additional, Krawitz, Peter M., additional, Köhler, Sebastian, additional, Laraway, Brian J., additional, Lehväslaiho, Heikki, additional, Lloyd, Kent C., additional, Matalonga, Leslie, additional, McMurry, Julie A., additional, Metke-Jimenez, Alejandro, additional, Mungall, Christopher J., additional, Munoz-Torres, Monica C., additional, Ogishima, Soichi, additional, Papakonstantinou, Anastasios, additional, Piscia, Davide, additional, Pontikos, Nikolas, additional, Queralt-Rosinach, Núria, additional, Roos, Marco, additional, Schofield, Paul N., additional, Siapos, Anastasios, additional, Smedley, Damian, additional, Smith, Lindsay D., additional, Steinhaus, Robin, additional, Sundaramurthi, Jagadish Chandrabose, additional, Swietlik, Emilia M., additional, Thun, Sylvia, additional, Vasilevsky, Nicole A., additional, Wagner, Alex H., additional, Warner, Jeremy L., additional, Weiland, Claus, additional, Haendel, Melissa A., additional, and Robinson, Peter N., additional

Published
2021
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39. The COVID-19 epidemiology and monitoring ontology

Author

Queralt-Rosinach, Núria, primary, Schofield, Paul, additional, Hoehndorf, Robert, additional, Weiland, Claus, additional, Schultes, Erik Anthony, additional, Bernabé, César Henrique, additional, and Roos, Marco, additional

Published
2021
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41. Semantic modelling of Common Data Elements for Rare Disease registries, and a prototype workflow for their deployment over registry data

Author

Kaliyaperumal, Rajaram, primary, Wilkinson, Mark D., additional, Alarcón Moreno, Pablo, additional, Benis, Nirupama, additional, Cornet, Ronald, additional, dos Santos Vieira, Bruna, additional, Dumontier, Michel, additional, Bernabé, César Henrique, additional, Jacobsen, Annika, additional, Le Cornec, Clémence M. A., additional, Godoy, Mario Prieto, additional, Queralt-Rosinach, Núria, additional, Schultze Kool, Leo J, additional, Swertz, Morris A., additional, van Damme, Philip, additional, van der Velde, K. Joeri, additional, van Lin, Nawel, additional, Zhang, Shuxin, additional, and Roos, Marco, additional

Published
2021
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42. Steeds FAIRder: verslag van het Urban Vitality zaaigeldproject 'FAIR: geen woorden maar data'

Author

van Ulzen, Niek, de Jong, Anne, Kanis, Marije, Kaliyaperumal, Rajaram, Queralt-Rosinach, Núria, Roos, Marco, Cornet, Ronald, Urban Vitality, and Lectoraat Digital Life

Abstract

Report of the project 'FAIR: geen woorden maar data' about the FAIRification of research data (in Dutch). It describes the proof of concept for implementation of the FAIR principles. The implementation is based on the resource description framework (RDF) and semantic knowledge representations using ontologies.

Published
2020

47. Global analysis of human SARS-CoV-2 infection and host-virus interaction

Author

Ferrarini, Mariana, primary, Aguiar-Pulido, Vanessa, additional, Dawson, Eric T., additional, Guarracino, Andrea, additional, Gruber, Andreas, additional, Heumos, Lukas, additional, Kanitz, Alexander, additional, Lal, Avantika, additional, Pickett, Brett E., additional, Rebollo, Rita, additional, Ruiz-Arenas, Carlos, additional, Awe, Olaitan I., additional, Bedi, Suhana, additional, Busby, Ben, additional, Georgaki, Marielena, additional, James, Chela, additional, Gonzalez, Itziar Martinez, additional, Meldal, Birgit, additional, Mucha, Scheila G., additional, Nabavi, Noushin, additional, Neiro, Jakke, additional, Queralt-Rosinach, Núria, additional, Rocca-Serra, Philippe, additional, de Oliveira, Daniel Siqueira, additional, and Tsagiopoulou, Maria, additional

Published
2020
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48. Data validation and schema interoperability

Author

Garcia Castro, Leyla Jael, primary, Bolleman, Jerven, additional, dumontier, michel, additional, Jupp, Simon, additional, Labra-Gayo, Jose Emilio, additional, Liener, Thomas, additional, Ohta, Tazro, additional, Queralt-Rosinach, Núria, additional, and Wu, Chunlei, additional

Published
2020
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49. BioHackathon 2015: Semantics of data for life sciences and reproducible research

Author

Vos, Rutger A., primary, Katayama, Toshiaki, additional, Mishima, Hiroyuki, additional, Kawano, Shin, additional, Kawashima, Shuichi, additional, Kim, Jin-Dong, additional, Moriya, Yuki, additional, Tokimatsu, Toshiaki, additional, Yamaguchi, Atsuko, additional, Yamamoto, Yasunori, additional, Wu, Hongyan, additional, Amstutz, Peter, additional, Antezana, Erick, additional, Aoki, Nobuyuki P., additional, Arakawa, Kazuharu, additional, Bolleman, Jerven T., additional, Bolton, Evan E., additional, Bonnal, Raoul J. P., additional, Bono, Hidemasa, additional, Burger, Kees, additional, Chiba, Hirokazu, additional, Cohen, Kevin B., additional, Deutsch, Eric W., additional, Fernández-Breis, Jesualdo T., additional, Fu, Gang, additional, Fujisawa, Takatomo, additional, Fukushima, Atsushi, additional, García, Alexander, additional, Goto, Naohisa, additional, Groza, Tudor, additional, Hercus, Colin, additional, Hoehndorf, Robert, additional, Itaya, Kotone, additional, Juty, Nick, additional, Kawashima, Takeshi, additional, Kim, Jee-Hyub, additional, Kinjo, Akira R., additional, Kotera, Masaaki, additional, Kozaki, Kouji, additional, Kumagai, Sadahiro, additional, Kushida, Tatsuya, additional, Lütteke, Thomas, additional, Matsubara, Masaaki, additional, Miyamoto, Joe, additional, Mohsen, Attayeb, additional, Mori, Hiroshi, additional, Naito, Yuki, additional, Nakazato, Takeru, additional, Nguyen-Xuan, Jeremy, additional, Nishida, Kozo, additional, Nishida, Naoki, additional, Nishide, Hiroyo, additional, Ogishima, Soichi, additional, Ohta, Tazro, additional, Okuda, Shujiro, additional, Paten, Benedict, additional, Perret, Jean-Luc, additional, Prathipati, Philip, additional, Prins, Pjotr, additional, Queralt-Rosinach, Núria, additional, Shinmachi, Daisuke, additional, Suzuki, Shinya, additional, Tabata, Tsuyosi, additional, Takatsuki, Terue, additional, Taylor, Kieron, additional, Thompson, Mark, additional, Uchiyama, Ikuo, additional, Vieira, Bruno, additional, Wei, Chih-Hsuan, additional, Wilkinson, Mark, additional, Yamada, Issaku, additional, Yamanaka, Ryota, additional, Yoshitake, Kazutoshi, additional, Yoshizawa, Akiyasu C., additional, Dumontier, Michel, additional, Kosaki, Kenjiro, additional, and Takagi, Toshihisa, additional

Published
2020
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50. FAIR Data Management to Access Patient Data

Author

Queralt Rosinach, Núria, Kaliyaperumal, Rajaram, Bernabé, César, Long, Qinqin, Van Der Wijk, Henk Jan, Mons, Barend, and Roos, Marco

Subjects
COVID-19, Research Data Management, Open Science, FAIR Data Point, Semantic Web, FAIR Data
Abstract

The COVID-19 pandemic is challenging healthcare systems and research worldwide. Clinical observations in hospitalised patients are not ready to use efficiently and timely neither by humans nor by machines. The Leiden University Medical Center (LUMC) clinicians and researchers have united to adapt the Research Data Management (RDM) in the hospital in order to make patient data more Findable, Accessible, Interoperable and Reusable (FAIR) for humans and machines. In this paper, we present our FAIRification plan for data management to transform COVID-19 observational patient data collected in the hospital into FAIR data. Our work demonstrates that a FAIR RDM based on open Science, Semantic Web technologies, and FAIR Data Points (FDPs) is providing data infrastructure in the clinics for machine-actionable FAIR research objec

Published
2020

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