121 results on '"Waltemath D"'
Search Results
2. NFDI4Health – nationale Forschungsdateninfrastruktur für personenbezogene Gesundheitsdaten
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Fluck, J., Lindstädt, B., Ahrens, W., Beyan, O., Buchner, B., Darms, J., Depping, R., Dierkes, J., Fröhlich, H., Gehrke, J., Golebiewski, M., Grabenhenrich, L., Hahn, H.K., Kirsten, T., Klammt, S., Kusch, H., Löbe, M., Löffler, M., Meineke, F., Müller, W., Neuhausen, H., Nöthlings, U., Pischon, T., Prasser, F., Sax, U., Schmidt, C.O., Schulze, M., Semler, S.C., Thun, S., Waltemath, D., Wieler, L.H., Zeeb, H., Pigeot, I., and Publica
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Cardiovascular and Metabolic Diseases ,ddc:340 - Abstract
Epidemiologische und klinische Studien sind standardisiert und gut dokumentiert, jedoch erfüllen Studienprotokolle, eingesetzte Erhebungsinstrumente und erhobene Daten die Anforderungen der FAIR-Prinzipien nicht in ausreichendem Maße. NFDI4Health wird daher eine Struktur schaffen, die eine zentrale Suche nach existierenden, dezentral verwalteten Datenkörpern und zugehörigen Dokumenten sowie einen FAIRen Zugang zu diesen erleichtert. Dazu werden die Auffindbarkeit und der Zugang zu strukturierten Gesundheitsdaten aus Registern, administrativen Gesundheitsdatenbanken, klinischen und epidemiologischen sowie Public Health-Studien verbessert und die Qualität und Harmonisierung der zugrundeliegenden Daten optimiert. Eine weitere Herausforderung entsteht durch die Verwendung personenbezogener Gesundheitsdaten. Diese sind hoch sensibel, so dass ihre Nutzung restriktive Datenschutzbestimmungen und informierte Einwilligungserklärungen der StudienteilnehmerInnen erfordert, was jedoch ihre Wiederverwendbarkeit einschränkt. NFDI4Health zielt daher darauf ab, den Austausch und die Verknüpfung von personenbezogenen Gesundheitsdaten sowie verteilte Datenanalysen unter Einhaltung datenschutzrechtlicher und ethischer Bestimmungen zu erleichtern. Um dies möglichst effizient zu erreichen, wird NFDI4Health die Entwicklung neuer, maschinenprozessierbarer Zustimmungsmöglichkeiten sowie innovativer Datenzugriffsservices auf Grundlage der FAIRPrinzipien vorantreiben und die Interoperabilität von IT-Lösungen für Metadatenrepositorien stärken. Komplementiert wird dies durch die Entwicklung entsprechender Angebote für Training und Ausbildung, um der Herausforderung der Umsetzung der Lösungen in den Universitäten und Forschungseinrichtungen zu begegnen. Schließlich wird durch die gemeinsame Arbeit in der NFDI4Health die Kooperation zwischen klinischer und epidemiologischer/Public Health-Forschung gestärkt.
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- 2022
3. Extending a COVID-19 knowledge graph with study protocols
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Gütebier, L, Henkel, R, and Waltemath, D
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ddc: 610 ,Medicine and health ,graph database ,COVID-19 ,clinical studies - Abstract
Introduction: A systematic approach for the representation and integration of data is essential for research data recycling and knowledge gain. The integration of data sources in a graph database, especially in health and life sciences, allows for time-efficient data exploration, deduction of semantic [for full text, please go to the a.m. URL]
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- 2022
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4. Challenges and potential bottlenecks for the accomplishment of provenance in biomedical data sets and workflows
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Gierend, K, Krüger, F, Genehr, S, Hartmann, F, Ganslandt, T, Waltemath, D, Zeleke, AA, Gierend, K, Krüger, F, Genehr, S, Hartmann, F, Ganslandt, T, Waltemath, D, and Zeleke, AA
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- 2022
5. Comparing Voluntary LOINC Mappings for the SHIP-4 Medical Laboratory Data Dictionary Before and After Domain Expert Review
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Inau, E, Radke, D, Westphal, S, Zeleke, AA, Waltemath, D, Inau, E, Radke, D, Westphal, S, Zeleke, AA, and Waltemath, D
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- 2022
6. Data Quality in Secondary Use: Adopting Concepts from Continous Risk Management – A Discussion Paper
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Bienzeisler, J, Triefenbach, L, Kombeiz, A, Lipprandt, M, Majeed, RW, Fischer, H, Otto, R, Zeleke, AA, Waltemath, D, and Röhrig, R
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ddc: 610 ,data quality assessment ,real-world data ,clinical data research network ,Medicine and health ,electronic health record ,risk management - Abstract
Introduction: Electronic health records (EHR) document medical treatment. The trustworthiness of the underlying data is one of the prerequisites for successful data and information reuse [ref:1], [ref:2], [ref:3]. As for all secondary data sources, the quality of data is [for full text, please go to the a.m. URL]
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- 2021
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7. How FAIR are frameworks for data quality measures in clinical research?
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Waltemath, D, Inau, E, Zeleke, AA, Schmidt, CO, Waltemath, D, Inau, E, Zeleke, AA, and Schmidt, CO
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- 2021
8. Comparative analysis of electronic versus paper-based data quality and cost-effectiveness outcomes in interviewer-administered public health surveys: a systematic review
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Zeleke, AA, Naziyok, TP, Fritz, F, Christianson, L, Waltemath, D, Röhrig, R, Zeleke, AA, Naziyok, TP, Fritz, F, Christianson, L, Waltemath, D, and Röhrig, R
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- 2021
9. The simulation experiment description markup language (SED-ML): language specification for level 1 version 5
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Smith Lucian P., Bergmann Frank T., Garny Alan, Helikar Tomáš, Karr Jonathan, Nickerson David, Sauro Herbert, Waltemath Dagmar, and König Matthias
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Biotechnology ,TP248.13-248.65 - Abstract
Modern biological research is increasingly informed by computational simulation experiments, which necessitate the development of methods for annotating, archiving, sharing, and reproducing the conducted experiments. These simulations increasingly require extensive collaboration among modelers, experimentalists, and engineers. The Minimum Information About a Simulation Experiment (MIASE) guidelines outline the information needed to share simulation experiments. SED-ML is a computer-readable format for the information outlined by MIASE, created as a community project and supported by many investigators and software tools. Level 1 Version 5 of SED-ML expands the ability of modelers to define simulations in SED-ML using the Kinetic Simulation Algorithm Onotoloy (KiSAO). While it was possible in Version 4 to define a simulation entirely using KiSAO, Version 5 now allows users to define tasks, model changes, ranges, and outputs using the ontology as well. SED-ML is supported by a growing ecosystem of investigators, model languages, and software tools, including various languages for constraint-based, kinetic, qualitative, rule-based, and spatial models, and many simulation tools, visual editors, model repositories, and validators. Additional information about SED-ML is available at https://sed-ml.org/.
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- 2024
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10. SBML Level 3: an extensible format for the exchange and reuse of biological models
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Keating, S, Waltemath, D, König, M, Zhang, F, Dräger, A, Chaouiya, C, Bergmann, F, Finney, A, Gillespie, C, Helikar, T, Hoops, S, Malik-Sheriff, R, Moodie, S, Moraru, I, Myers, C, Naldi, A, Olivier, B, Sahle, S, Schaff, J, Smith, L, Swat, M, Thieffry, D, Watanabe, L, Wilkinson, D, Blinov, M, Begley, K, Faeder, J, Gómez, H, Hamm, T, Inagaki, Y, Liebermeister, W, Lister, A, Lucio, D, Mjolsness, E, Proctor, C, Raman, K, Rodriguez, N, Shaffer, C, Shapiro, B, Stelling, J, Swainston, N, Tanimura, N, Wagner, J, Meier-Schellersheim, M, Sauro, H, Palsson, B, Bolouri, H, Kitano, H, Funahashi, A, Hermjakob, H, Doyle, J, Hucka, M, Adams, R, Allen, N, Angermann, B, Antoniotti, M, Bader, G, Červený, J, Courtot, M, Cox, C, Dalle Pezze, P, Demir, E, Denney, W, Dharuri, H, Dorier, J, Drasdo, D, Ebrahim, A, Eichner, J, Elf, J, Endler, L, Evelo, C, Flamm, C, Fleming, R, Fröhlich, M, Glont, M, Gonçalves, E, Golebiewski, M, Grabski, H, Gutteridge, A, Hachmeister, D, Harris, L, Heavner, B, Henkel, R, Hlavacek, W, Hu, B, Hyduke, D, Jong, H, Juty, N, Karp, P, Karr, J, Kell, D, Keller, R, Kiselev, I, Klamt, S, Klipp, E, Knüpfer, C, Kolpakov, F, Krause, F, Kutmon, M, Laibe, C, Lawless, C, Li, L, Loew, L, Machne, R, Matsuoka, Y, Mendes, P, Mi, H, Mittag, F, Monteiro, P, Natarajan, K, Nielsen, P, Nguyen, T, Palmisano, A, Jean-Baptiste, P, Pfau, T, Phair, R, Radivoyevitch, T, Rohwer, J, Ruebenacker, O, Saez-Rodriguez, J, Scharm, M, Schmidt, H, Schreiber, F, Schubert, M, Schulte, R, Sealfon, S, Smallbone, K, Soliman, S, Stefan, M, Sullivan, D, Takahashi, K, Teusink, B, Tolnay, D, Vazirabad, I, Kamp, A, Wittig, U, Wrzodek, C, Wrzodek, F, Xenarios, I, Zhukova, A, Zucker, J, Keating, SM, Bergmann, FT, Gillespie, CS, Malik-Sheriff, RS, Moodie, SL, Moraru, II, Myers, CJ, Olivier, BG, Schaff, JC, Smith, LP, Swat, MJ, Wilkinson, DJ, Blinov, ML, Faeder, JR, Gómez, HF, Hamm, TM, Lister, AL, Proctor, CJ, Shaffer, CA, Shapiro, BE, Sauro, HM, Doyle, JC, Adams, RR, Allen, NA, Angermann, BR, Bader, GD, Cox, CD, Denney, WS, Evelo, CT, Fleming, RM, Harris, LA, Heavner, BD, Hlavacek, WS, Hyduke, DR, Karp, PD, Karr, JR, Kell, DB, Loew, LM, Monteiro, PT, Natarajan, KN, Nielsen, PM, Phair, RD, Rohwer, JM, Ruebenacker, OA, Sealfon, SC, Stefan, MI, Sullivan, DP, Keating, S, Waltemath, D, König, M, Zhang, F, Dräger, A, Chaouiya, C, Bergmann, F, Finney, A, Gillespie, C, Helikar, T, Hoops, S, Malik-Sheriff, R, Moodie, S, Moraru, I, Myers, C, Naldi, A, Olivier, B, Sahle, S, Schaff, J, Smith, L, Swat, M, Thieffry, D, Watanabe, L, Wilkinson, D, Blinov, M, Begley, K, Faeder, J, Gómez, H, Hamm, T, Inagaki, Y, Liebermeister, W, Lister, A, Lucio, D, Mjolsness, E, Proctor, C, Raman, K, Rodriguez, N, Shaffer, C, Shapiro, B, Stelling, J, Swainston, N, Tanimura, N, Wagner, J, Meier-Schellersheim, M, Sauro, H, Palsson, B, Bolouri, H, Kitano, H, Funahashi, A, Hermjakob, H, Doyle, J, Hucka, M, Adams, R, Allen, N, Angermann, B, Antoniotti, M, Bader, G, Červený, J, Courtot, M, Cox, C, Dalle Pezze, P, Demir, E, Denney, W, Dharuri, H, Dorier, J, Drasdo, D, Ebrahim, A, Eichner, J, Elf, J, Endler, L, Evelo, C, Flamm, C, Fleming, R, Fröhlich, M, Glont, M, Gonçalves, E, Golebiewski, M, Grabski, H, Gutteridge, A, Hachmeister, D, Harris, L, Heavner, B, Henkel, R, Hlavacek, W, Hu, B, Hyduke, D, Jong, H, Juty, N, Karp, P, Karr, J, Kell, D, Keller, R, Kiselev, I, Klamt, S, Klipp, E, Knüpfer, C, Kolpakov, F, Krause, F, Kutmon, M, Laibe, C, Lawless, C, Li, L, Loew, L, Machne, R, Matsuoka, Y, Mendes, P, Mi, H, Mittag, F, Monteiro, P, Natarajan, K, Nielsen, P, Nguyen, T, Palmisano, A, Jean-Baptiste, P, Pfau, T, Phair, R, Radivoyevitch, T, Rohwer, J, Ruebenacker, O, Saez-Rodriguez, J, Scharm, M, Schmidt, H, Schreiber, F, Schubert, M, Schulte, R, Sealfon, S, Smallbone, K, Soliman, S, Stefan, M, Sullivan, D, Takahashi, K, Teusink, B, Tolnay, D, Vazirabad, I, Kamp, A, Wittig, U, Wrzodek, C, Wrzodek, F, Xenarios, I, Zhukova, A, Zucker, J, Keating, SM, Bergmann, FT, Gillespie, CS, Malik-Sheriff, RS, Moodie, SL, Moraru, II, Myers, CJ, Olivier, BG, Schaff, JC, Smith, LP, Swat, MJ, Wilkinson, DJ, Blinov, ML, Faeder, JR, Gómez, HF, Hamm, TM, Lister, AL, Proctor, CJ, Shaffer, CA, Shapiro, BE, Sauro, HM, Doyle, JC, Adams, RR, Allen, NA, Angermann, BR, Bader, GD, Cox, CD, Denney, WS, Evelo, CT, Fleming, RM, Harris, LA, Heavner, BD, Hlavacek, WS, Hyduke, DR, Karp, PD, Karr, JR, Kell, DB, Loew, LM, Monteiro, PT, Natarajan, KN, Nielsen, PM, Phair, RD, Rohwer, JM, Ruebenacker, OA, Sealfon, SC, Stefan, MI, and Sullivan, DP
- Abstract
Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.
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- 2020
11. Harmonizing semantic annotations for computational models in biology
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Neal, ML, primary, König, M, additional, Nickerson, D, additional, Mısırlı, G, additional, Kalbasi, R, additional, Dräger, A, additional, Atalag, K, additional, Chelliah, V, additional, Cooling, M, additional, Cook, DL, additional, Crook, S, additional, de Alba, M, additional, Friedman, SH, additional, Garny, A, additional, Gennari, JH, additional, Gleeson, P, additional, Golebiewski, M, additional, Hucka, M, additional, Juty, N, additional, Le Novère, N, additional, Myers, C, additional, Olivier, BG, additional, Sauro, HM, additional, Scharm, M, additional, Snoep, JL, additional, Touré, V, additional, Wipat, A, additional, Wolkenhauer, O, additional, and Waltemath, D, additional
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- 2018
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12. Specifications of standards in systems and synthetic biology: status and developments in 2022 and the COMBINE meeting 2022
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König Matthias, Gleeson Padraig, Golebiewski Martin, Gorochowski Thomas E., Hucka Michael, Keating Sarah M., Myers Chris J., Nickerson David P., Sommer Björn, Waltemath Dagmar, and Schreiber Falk
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Biotechnology ,TP248.13-248.65 - Abstract
This special issue of the Journal of Integrative Bioinformatics contains updated specifications of COMBINE standards in systems and synthetic biology. The 2022 special issue presents three updates to the standards: CellML 2.0.1, SBML Level 3 Package: Spatial Processes, Version 1, Release 1, and Synthetic Biology Open Language (SBOL) Version 3.1.0. This document can also be used to identify the latest specifications for all COMBINE standards. In addition, this editorial provides a brief overview of the COMBINE 2022 meeting in Berlin.
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- 2023
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13. Toward Community Standards and Software for Whole-Cell Modeling
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Waltemath, D, Karr, JR, Bergmann, FT, Chelliah, V, Hucka, M, Krantz, M, Liebermeister, W, Mendes, P, Myers, CJ, Pir, P, Alaybeyoglu, B, Aranganathan, NK, Baghalian, K, Bittig, AT, Pinto Burke, PE, Cantarelli, M, Chew, YH, Costa, RS, Cursons, J, Czauderna, T, Goldberg, AP, Gomez, HF, Hahn, J, Hameri, T, Gardiol, DFH, Kazakiewicz, D, Kiselev, I, Knight-Schrijver, V, Knuepfer, C, Koenig, M, Lee, D, Lloret-Villas, A, Mandrik, N, Medley, JK, Moreau, B, Naderi-Meshkin, H, Palaniappan, SK, Priego-Espinosa, D, Scharm, M, Sharma, M, Smallbone, K, Stanford, NJ, Song, J-H, Theile, T, Tokic, M, Tomar, N, Toure, V, Uhlendorf, J, Varusai, TM, Watanabe, LH, Wendland, F, Wolfien, M, Yurkovich, JT, Zhu, Y, Zardilis, A, Zhukova, A, Schreiber, F, Waltemath, D, Karr, JR, Bergmann, FT, Chelliah, V, Hucka, M, Krantz, M, Liebermeister, W, Mendes, P, Myers, CJ, Pir, P, Alaybeyoglu, B, Aranganathan, NK, Baghalian, K, Bittig, AT, Pinto Burke, PE, Cantarelli, M, Chew, YH, Costa, RS, Cursons, J, Czauderna, T, Goldberg, AP, Gomez, HF, Hahn, J, Hameri, T, Gardiol, DFH, Kazakiewicz, D, Kiselev, I, Knight-Schrijver, V, Knuepfer, C, Koenig, M, Lee, D, Lloret-Villas, A, Mandrik, N, Medley, JK, Moreau, B, Naderi-Meshkin, H, Palaniappan, SK, Priego-Espinosa, D, Scharm, M, Sharma, M, Smallbone, K, Stanford, NJ, Song, J-H, Theile, T, Tokic, M, Tomar, N, Toure, V, Uhlendorf, J, Varusai, TM, Watanabe, LH, Wendland, F, Wolfien, M, Yurkovich, JT, Zhu, Y, Zardilis, A, Zhukova, A, and Schreiber, F
- Abstract
OBJECTIVE: Whole-cell (WC) modeling is a promising tool for biological research, bioengineering, and medicine. However, substantial work remains to create accurate comprehensive models of complex cells. METHODS: We organized the 2015 Whole-Cell Modeling Summer School to teach WC modeling and evaluate the need for new WC modeling standards and software by recoding a recently published WC model in the Systems Biology Markup Language. RESULTS: Our analysis revealed several challenges to representing WC models using the current standards. CONCLUSION: We, therefore, propose several new WC modeling standards, software, and databases. SIGNIFICANCE: We anticipate that these new standards and software will enable more comprehensive models.
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- 2016
14. Specifications of standards in systems and synthetic biology: status and developments in 2021
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Schreiber Falk, Gleeson Padraig, Golebiewski Martin, Gorochowski Thomas E., Hucka Michael, Keating Sarah M., König Matthias, Myers Chris J., Nickerson David P., Sommer Björn, and Waltemath Dagmar
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Biotechnology ,TP248.13-248.65 - Abstract
This special issue of the Journal of Integrative Bioinformatics contains updated specifications of COMBINE standards in systems and synthetic biology. The 2021 special issue presents four updates of standards: Synthetic Biology Open Language Visual Version 2.3, Synthetic Biology Open Language Visual Version 3.0, Simulation Experiment Description Markup Language Level 1 Version 4, and OMEX Metadata specification Version 1.2. This document can also be consulted to identify the latest specifications of all COMBINE standards.
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- 2021
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15. Towards standardization guidelines for in silico approaches in personalized medicine
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Brunak Søren, Bjerre Collin Catherine, Eva Ó Cathaoir Katharina, Golebiewski Martin, Kirschner Marc, Kockum Ingrid, Moser Heike, and Waltemath Dagmar
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data integration ,in silico modelling ,personalized medicine ,reproducibility ,standards ,Biotechnology ,TP248.13-248.65 - Abstract
Despite the ever-progressing technological advances in producing data in health and clinical research, the generation of new knowledge for medical benefits through advanced analytics still lags behind its full potential. Reasons for this obstacle are the inherent heterogeneity of data sources and the lack of broadly accepted standards. Further hurdles are associated with legal and ethical issues surrounding the use of personal/patient data across disciplines and borders. Consequently, there is a need for broadly applicable standards compliant with legal and ethical regulations that allow interpretation of heterogeneous health data through in silico methodologies to advance personalized medicine. To tackle these standardization challenges, the Horizon2020 Coordinating and Support Action EU-STANDS4PM initiated an EU-wide mapping process to evaluate strategies for data integration and data-driven in silico modelling approaches to develop standards, recommendations and guidelines for personalized medicine. A first step towards this goal is a broad stakeholder consultation process initiated by an EU-STANDS4PM workshop at the annual COMBINE meeting (COMBINE 2019 workshop report in same issue). This forum analysed the status quo of data and model standards and reflected on possibilities as well as challenges for cross-domain data integration to facilitate in silico modelling approaches for personalized medicine.
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- 2020
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16. The first 10 years of the international coordination network for standards in systems and synthetic biology (COMBINE)
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Waltemath Dagmar, Golebiewski Martin, Blinov Michael L, Gleeson Padraig, Hermjakob Henning, Hucka Michael, Inau Esther Thea, Keating Sarah M, König Matthias, Krebs Olga, Malik-Sheriff Rahuman S, Nickerson David, Oberortner Ernst, Sauro Herbert M, Schreiber Falk, Smith Lucian, Stefan Melanie I, Wittig Ulrike, and Myers Chris J
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combine ,community building ,meeting report ,standardization ,Biotechnology ,TP248.13-248.65 - Abstract
This paper presents a report on outcomes of the 10th Computational Modeling in Biology Network (COMBINE) meeting that was held in Heidelberg, Germany, in July of 2019. The annual event brings together researchers, biocurators and software engineers to present recent results and discuss future work in the area of standards for systems and synthetic biology. The COMBINE initiative coordinates the development of various community standards and formats for computational models in the life sciences. Over the past 10 years, COMBINE has brought together standard communities that have further developed and harmonized their standards for better interoperability of models and data. COMBINE 2019 was co-located with a stakeholder workshop of the European EU-STANDS4PM initiative that aims at harmonized data and model standardization for in silico models in the field of personalized medicine, as well as with the FAIRDOM PALs meeting to discuss findable, accessible, interoperable and reusable (FAIR) data sharing. This report briefly describes the work discussed in invited and contributed talks as well as during breakout sessions. It also highlights recent advancements in data, model, and annotation standardization efforts. Finally, this report concludes with some challenges and opportunities that this community will face during the next 10 years.
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- 2020
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17. Specifications of standards in systems and synthetic biology: status and developments in 2020
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Schreiber Falk, Sommer Björn, Czauderna Tobias, Golebiewski Martin, Gorochowski Thomas E., Hucka Michael, Keating Sarah M., König Matthias, Myers Chris, Nickerson David, and Waltemath Dagmar
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ontologies ,standards ,systems biology ,synthetic biology ,Biotechnology ,TP248.13-248.65 - Abstract
This special issue of the Journal of Integrative Bioinformatics presents papers related to the 10th COMBINE meeting together with the annual update of COMBINE standards in systems and synthetic biology.
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- 2020
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18. The simulation experiment description markup language (SED-ML): language specification for level 1 version 4
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Smith Lucian P., Bergmann Frank T., Garny Alan, Helikar Tomáš, Karr Jonathan, Nickerson David, Sauro Herbert, Waltemath Dagmar, and König Matthias
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computational modeling ,reproducibility ,simulation experiment ,Biotechnology ,TP248.13-248.65 - Abstract
Computational simulation experiments increasingly inform modern biological research, and bring with them the need to provide ways to annotate, archive, share and reproduce the experiments performed. These simulations increasingly require extensive collaboration among modelers, experimentalists, and engineers. The Minimum Information About a Simulation Experiment (MIASE) guidelines outline the information needed to share simulation experiments. SED-ML is a computer-readable format for the information outlined by MIASE, created as a community project and supported by many investigators and software tools. The first versions of SED-ML focused on deterministic and stochastic simulations of models. Level 1 Version 4 of SED-ML substantially expands these capabilities to cover additional types of models, model languages, parameter estimations, simulations and analyses of models, and analyses and visualizations of simulation results. To facilitate consistent practices across the community, Level 1 Version 4 also more clearly describes the use of SED-ML constructs, and includes numerous concrete validation rules. SED-ML is supported by a growing ecosystem of investigators, model languages, and software tools, including eight languages for constraint-based, kinetic, qualitative, rule-based, and spatial models, over 20 simulation tools, visual editors, model repositories, and validators. Additional information about SED-ML is available at https://sed-ml.org/.
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- 2021
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19. OMEX metadata specification (version 1.2)
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Gennari John H., König Matthias, Misirli Goksel, Neal Maxwell L., Nickerson David P., and Waltemath Dagmar
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biosimulation modeling ,combine standards ,metadata ,semantics ,Biotechnology ,TP248.13-248.65 - Abstract
A standardized approach to annotating computational biomedical models and their associated files can facilitate model reuse and reproducibility among research groups, enhance search and retrieval of models and data, and enable semantic comparisons between models. Motivated by these potential benefits and guided by consensus across the COmputational Modeling in BIology NEtwork (COMBINE) community, we have developed a specification for encoding annotations in Open Modeling and EXchange (OMEX)-formatted archives. This document details version 1.2 of the specification, which builds on version 1.0 published last year in this journal. In particular, this version includes a set of initial model-level annotations (whereas v 1.0 described exclusively annotations at a smaller scale). Additionally, this version uses best practices for namespaces, and introduces omex-library.org as a common root for all annotations. Distributing modeling projects within an OMEX archive is a best practice established by COMBINE, and the OMEX metadata specification presented here provides a harmonized, community-driven approach for annotating a variety of standardized model representations. This specification acts as a technical guideline for developing software tools that can support this standard, and thereby encourages broad advances in model reuse, discovery, and semantic analyses.
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- 2021
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20. Minimum information about a simulation experiment (MIASE).
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Waltemath, D., Adams, R., Beard, D.A., Bergmann, F.T., Bhalla, U.S., Britten, R., Chelliah, V., Cooling, M.T., Cooper, J., Crampin, E.J., Garny, A., Hoops, S., Hucka, M., Hunter, P., Klipp, E., Laibe, C., Miller, A.K., Moraru, K., Nickerson, D., Nielsen, P., Nikolski, M., Sahle, S., Sauro, H.M., Schmidt, H., Snoep, J.L., Tolle, D., Wolkenhauer, O., le Novere, N., Waltemath, D., Adams, R., Beard, D.A., Bergmann, F.T., Bhalla, U.S., Britten, R., Chelliah, V., Cooling, M.T., Cooper, J., Crampin, E.J., Garny, A., Hoops, S., Hucka, M., Hunter, P., Klipp, E., Laibe, C., Miller, A.K., Moraru, K., Nickerson, D., Nielsen, P., Nikolski, M., Sahle, S., Sauro, H.M., Schmidt, H., Snoep, J.L., Tolle, D., Wolkenhauer, O., and le Novere, N.
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- 2011
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21. Minimum Information About a Simulation Experiment (MIASE)
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Bourne, PE, Waltemath, D, Adams, R, Beard, DA, Bergmann, FT, Bhalla, US, Britten, R, Chelliah, V, Cooling, MT, Cooper, J, Crampin, EJ, Garny, A, Hoops, S, Hucka, M, Hunter, P, Klipp, E, Laibe, C, Miller, AK, Moraru, I, Nickerson, D, Nielsen, P, Nikolski, M, Sahle, S, Sauro, HM, Schmidt, H, Snoep, JL, Tolle, D, Wolkenhauer, O, Le Novere, N, Bourne, PE, Waltemath, D, Adams, R, Beard, DA, Bergmann, FT, Bhalla, US, Britten, R, Chelliah, V, Cooling, MT, Cooper, J, Crampin, EJ, Garny, A, Hoops, S, Hucka, M, Hunter, P, Klipp, E, Laibe, C, Miller, AK, Moraru, I, Nickerson, D, Nielsen, P, Nikolski, M, Sahle, S, Sauro, HM, Schmidt, H, Snoep, JL, Tolle, D, Wolkenhauer, O, and Le Novere, N
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- 2011
22. Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2019
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Schreiber Falk, Sommer Björn, Bader Gary D., Gleeson Padraig, Golebiewski Martin, Hucka Michael, Keating Sarah M., König Matthias, Myers Chris, Nickerson David, and Waltemath Dagmar
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Biotechnology ,TP248.13-248.65 - Abstract
This special issue of the Journal of Integrative Bioinformatics presents an overview of COMBINE standards and their latest specifications. The standards cover representation formats for computational modeling in synthetic and systems biology and include BioPAX, CellML, NeuroML, SBML, SBGN, SBOL and SED-ML. The articles in this issue contain updated specifications of SBGN Process Description Level 1 Version 2, SBML Level 3 Core Version 2 Release 2, SBOL Version 2.3.0, and SBOL Visual Version 2.1.
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- 2019
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23. Open modeling and exchange (OMEX) metadata specification version 1.0
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Neal Maxwell L., Gennari John H., Waltemath Dagmar, Nickerson David P., and König Matthias
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data integration ,metadata ,semantics ,simulation ,Biotechnology ,TP248.13-248.65 - Abstract
A standardized approach to annotating computational biomedical models and their associated files can facilitate model reuse and reproducibility among research groups, enhance search and retrieval of models and data, and enable semantic comparisons between models. Motivated by these potential benefits and guided by consensus across the COmputational Modeling in BIology NEtwork (COMBINE) community, we have developed a specification for encoding annotations in Open Modeling and EXchange (OMEX)-formatted archives. Distributing modeling projects within these archives is a best practice established by COMBINE, and the OMEX metadata specification presented here provides a harmonized, community-driven approach for annotating a variety of standardized model and data representation formats within an archive. The specification primarily includes technical guidelines for encoding archive metadata, so that software tools can more easily utilize and exchange it, thereby spurring broad advancements in model reuse, discovery, and semantic analyses.
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- 2020
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24. Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2017
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Schreiber Falk, Bader Gary D., Gleeson Padraig, Golebiewski Martin, Hucka Michael, Keating Sarah M., Novère Nicolas Le, Myers Chris, Nickerson David, Sommer Björn, and Waltemath Dagmar
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combine ,systems biology ,synthetic biology ,standards ,Biotechnology ,TP248.13-248.65 - Abstract
Standards are essential to the advancement of Systems and Synthetic Biology. COMBINE provides a formal body and a centralised platform to help develop and disseminate relevant standards and related resources. The regular special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards by providing unified, easily citable access. This paper provides an overview of existing COMBINE standards and presents developments of the last year.
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- 2018
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25. The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core Release 2
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Hucka Michael, Bergmann Frank T., Chaouiya Claudine, Dräger Andreas, Hoops Stefan, Keating Sarah M., König Matthias, Novère Nicolas Le, Myers Chris J., Olivier Brett G., Sahle Sven, Schaff James C., Sheriff Rahuman, Smith Lucian P., Waltemath Dagmar, Wilkinson Darren J., and Zhang Fengkai
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systems biology markup language ,standards ,visualization ,representation ,Biotechnology ,TP248.13-248.65 - Abstract
Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Release 2 of Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. Release 2 corrects some errors and clarifies some ambiguities discovered in Release 1. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project website at http://sbml.org/.
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- 2019
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26. Specifications of Standards in Systems and Synthetic Biology: Status and Developments in 2016
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Schreiber Falk, Bader Gary D., Gleeson Padraig, Golebiewski Martin, Hucka Michael, Novère Nicolas Le, Myers Chris, Nickerson David, Sommer Björn, and Waltemath Dagmar
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Biotechnology ,TP248.13-248.65 - Abstract
Standards are essential to the advancement of science and technology. In systems and synthetic biology, numerous standards and associated tools have been developed over the last 16 years. This special issue of the Journal of Integrative Bioinformatics aims to support the exchange, distribution and archiving of these standards, as well as to provide centralised and easily citable access to them.
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- 2016
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27. The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 1 Core
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Hucka Michael, Bergmann Frank T., Dräger Andreas, Hoops Stefan, Keating Sarah M., Le Novère Nicolas, Myers Chris J., Olivier Brett G., Sahle Sven, Schaff James C., Smith Lucian P., Waltemath Dagmar, and Wilkinson Darren J.
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sbml ,modeling ,standards ,Biotechnology ,TP248.13-248.65 - Abstract
Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Release 2 of Version 1 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML, their encoding in XML (the eXtensible Markup Language), validation rules that determine the validity of an SBML document, and examples of models in SBML form. No design changes have been made to the description of models between Release 1 and Release 2; changes are restricted to the format of annotations, the correction of errata and the addition of clarifications. Other materials and software are available from the SBML project website at http://sbml.org/.
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- 2018
- Full Text
- View/download PDF
28. Simulation Experiment Description Markup Language (SED-ML) Level 1 Version 3 (L1V3)
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Bergmann Frank T., Cooper Jonathan, König Matthias, Moraru Ion, Nickerson David, Le Novère Nicolas, Olivier Brett G., Sahle Sven, Smith Lucian, and Waltemath Dagmar
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simulation experiment ,computational modeling ,reproducibility ,Biotechnology ,TP248.13-248.65 - Abstract
The creation of computational simulation experiments to inform modern biological research poses challenges to reproduce, annotate, archive, and share such experiments. Efforts such as SBML or CellML standardize the formal representation of computational models in various areas of biology. The Simulation Experiment Description Markup Language (SED-ML) describes what procedures the models are subjected to, and the details of those procedures. These standards, together with further COMBINE standards, describe models sufficiently well for the reproduction of simulation studies among users and software tools. The Simulation Experiment Description Markup Language (SED-ML) is an XML-based format that encodes, for a given simulation experiment, (i) which models to use; (ii) which modifications to apply to models before simulation; (iii) which simulation procedures to run on each model; (iv) how to post-process the data; and (v) how these results should be plotted and reported. SED-ML Level 1 Version 1 (L1V1) implemented support for the encoding of basic time course simulations. SED-ML L1V2 added support for more complex types of simulations, specifically repeated tasks and chained simulation procedures. SED-ML L1V3 extends L1V2 by means to describe which datasets and subsets thereof to use within a simulation experiment.
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- 2018
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29. The Systems Biology Markup Language (SBML): Language Specification for Level 3 Version 2 Core
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Hucka Michael, Bergmann Frank T., Dräger Andreas, Hoops Stefan, Keating Sarah M., Le Novère Nicolas, Myers Chris J., Olivier Brett G., Sahle Sven, Schaff James C., Smith Lucian P., Waltemath Dagmar, and Wilkinson Darren J.
- Subjects
sbml ,modeling ,computational biology ,systems biology ,standards ,Biotechnology ,TP248.13-248.65 - Abstract
Computational models can help researchers to interpret data, understand biological functions, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that different software systems can exchange. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 2 of SBML Level 3 Core. The specification defines the data structures prescribed by SBML, their encoding in XML (the eXtensible Markup Language), validation rules that determine the validity of an SBML document, and examples of models in SBML form. The design of Version 2 differs from Version 1 principally in allowing new MathML constructs, making more child elements optional, and adding identifiers to all SBML elements instead of only selected elements. Other materials and software are available from the SBML project website at http://sbml.org/.
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- 2018
- Full Text
- View/download PDF
30. Systems Biology Markup Language (SBML) Level 2 Version 5: Structures and Facilities for Model Definitions
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Hucka Michael, Bergmann Frank T., Dräger Andreas, Hoops Stefan, Keating Sarah M., Le Novère Nicolas, Myers Chris J., Olivier Brett G., Sahle Sven, Schaff James C., Smith Lucian P., Waltemath Dagmar, and Wilkinson Darren J.
- Subjects
Biotechnology ,TP248.13-248.65 - Abstract
Computational models can help researchers to interpret data, understand biological function, and make quantitative predictions. The Systems Biology Markup Language (SBML) is a file format for representing computational models in a declarative form that can be exchanged between different software systems. SBML is oriented towards describing biological processes of the sort common in research on a number of topics, including metabolic pathways, cell signaling pathways, and many others. By supporting SBML as an input/output format, different tools can all operate on an identical representation of a model, removing opportunities for translation errors and assuring a common starting point for analyses and simulations. This document provides the specification for Version 5 of SBML Level 2. The specification defines the data structures prescribed by SBML as well as their encoding in XML, the eXtensible Markup Language. This specification also defines validation rules that determine the validity of an SBML document, and provides many examples of models in SBML form. Other materials and software are available from the SBML project web site, http://sbml.org/.
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- 2015
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31. Simulation Experiment Description Markup Language (SED-ML) Level 1 Version 2
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Bergmann Frank T., Cooper Jonathan, Le Novère Nicolas, Nickerson David, and Waltemath Dagmar
- Subjects
Biotechnology ,TP248.13-248.65 - Abstract
The number, size and complexity of computational models of biological systems are growing at an ever increasing pace. It is imperative to build on existing studies by reusing and adapting existing models and parts thereof. The description of the structure of models is not sufficient to enable the reproduction of simulation results. One also needs to describe the procedures the models are subjected to, as recommended by the Minimum Information About a Simulation Experiment (MIASE) guidelines.
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- 2015
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32. Specifications of Standards in Systems and Synthetic Biology
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Schreiber Falk, Bader Gary D., Golebiewski Martin, Hucka Michael, Kormeier Benjamin, Le Novère Nicolas, Myers Chris, Nickerson David, Sommer Björn, Waltemath Dagmar, and Weise Stephan
- Subjects
Biotechnology ,TP248.13-248.65 - Abstract
Standards shape our everyday life. From nuts and bolts to electronic devices and technological processes, standardised products and processes are all around us. Standards have technological and economic benefits, such as making information exchange, production, and services more efficient. However, novel, innovative areas often either lack proper standards, or documents about standards in these areas are not available from a centralised platform or formal body (such as the International Standardisation Organisation).
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- 2015
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33. Toward community standards and software for whole-cell modeling
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Waltemath D, Karr J, Bergmann F, Chelliah V, Hucka M, Krantz M, Liebermeister W, Mendes P, Myers C, Pir P, Begum Alaybeyoglu, Aranganathan N, Baghalian K, Bittig A, Burke P, Cantarelli M, Chew Y, Costa R, Cursons J, and Czauderna T
34. Meeting report from the fourth meeting of the Computational Modeling in Biology Network (COMBINE)
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Waltemath D, Ft, Bergmann, Chaouiya C, Czauderna T, Gleeson P, Goble C, Golebiewski M, Hucka M, Navtej Juty, Krebs O, Le Novère N, Mi H, Ii, Moraru, Cj, Myers, Nickerson D, Bg, Olivier, Rodriguez N, Schreiber F, Smith L, and Zhang F
35. Ranked retrieval of Computational Biology models
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Henkel Ron, Endler Lukas, Peters Andre, Le Novère Nicolas, and Waltemath Dagmar
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Computer applications to medicine. Medical informatics ,R858-859.7 ,Biology (General) ,QH301-705.5 - Abstract
Abstract Background The study of biological systems demands computational support. If targeting a biological problem, the reuse of existing computational models can save time and effort. Deciding for potentially suitable models, however, becomes more challenging with the increasing number of computational models available, and even more when considering the models' growing complexity. Firstly, among a set of potential model candidates it is difficult to decide for the model that best suits ones needs. Secondly, it is hard to grasp the nature of an unknown model listed in a search result set, and to judge how well it fits for the particular problem one has in mind. Results Here we present an improved search approach for computational models of biological processes. It is based on existing retrieval and ranking methods from Information Retrieval. The approach incorporates annotations suggested by MIRIAM, and additional meta-information. It is now part of the search engine of BioModels Database, a standard repository for computational models. Conclusions The introduced concept and implementation are, to our knowledge, the first application of Information Retrieval techniques on model search in Computational Systems Biology. Using the example of BioModels Database, it was shown that the approach is feasible and extends the current possibilities to search for relevant models. The advantages of our system over existing solutions are that we incorporate a rich set of meta-information, and that we provide the user with a relevance ranking of the models found for a query. Better search capabilities in model databases are expected to have a positive effect on the reuse of existing models.
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- 2010
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36. SBML Level 3: an extensible format for the exchange and reuse of biological models
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Edda Klipp, Marco Antoniotti, Frank Bergmann, James C. Schaff, Peter D. Karp, Daniel Lucio, Kedar Nath Natarajan, Thomas M. Hamm, Leandro Watanabe, Henning Hermjakob, David Tolnay, John Wagner, Joerg Stelling, Alida Palmisano, Falk Schreiber, Yukiko Matsuoka, Harold F. Gómez, Huaiyu Mi, Carole J. Proctor, Ulrike Wittig, Neil Swainston, Jan Červený, Denis Thieffry, Piero Dalle Pezze, Julio Saez-Rodriguez, Maciej J. Swat, Bin Hu, Martina Kutmon, Thomas Pfau, Bas Teusink, Sarah M. Keating, Fedor A. Kolpakov, Andreas Dräger, Pedro Mendes, Martin Scharm, Emek Demir, Ioannis Xenarios, Christoph Flamm, Axel von Kamp, Darren J. Wilkinson, Nick Juty, Fengkai Zhang, Leonard A. Harris, Michael Schubert, Dagmar Waltemath, Lucian P. Smith, Steffen Klamt, Herbert M. Sauro, Ali Ebrahim, Wolfram Liebermeister, Christian Knüpfer, Nicolas Rodriguez, Tramy Nguyen, Naoki Tanimura, Christopher Cox, Stuart C. Sealfon, Nicholas Alexander Allen, Clemens Wrzodek, Bastian R. Angermann, Martin Meier-Schellersheim, Anna Zhukova, Jean-Baptiste Pettit, Hovakim Grabski, Devin P. Sullivan, Claudine Chaouiya, Michael L. Blinov, John Doyle, Ilya Kiselev, Roman Schulte, Alex Gutteridge, Mélanie Courtot, Eric Mjolsness, Finja Wrzodek, Rahuman S Malik-Sheriff, Ronan M. T. Fleming, Bruce E. Shapiro, Kimberly Begley, Leslie M. Loew, Colin S. Gillespie, Ibrahim Vazirabad, Michael Hucka, Akira Funahashi, Bernhard O. Palsson, Hamid Bolouri, Tomáš Helikar, Camille Laibe, William S. Denney, Chris T. Evelo, Florian Mittag, William S. Hlavacek, Ron Henkel, Harish Dharuri, Julien Dorier, Karthik Raman, Martina Fröhlich, Conor Lawless, Rainer Machné, Falko Krause, Damon Hachmeister, Matthias König, Clifford A. Shaffer, Benjamin D. Heavner, Douglas B. Kell, Jonathan R. Karr, Mihai Glont, Lukas Endler, Melanie I. Stefan, Robert Phair, Lu Li, Henning Schmidt, Dirk Drasdo, Johan Elf, Allyson L. Lister, Hiroaki Kitano, Richard R. Adams, Oliver A. Ruebenacker, Roland Keller, Sven Sahle, Ion I. Moraru, Gary D. Bader, Poul M. F. Nielsen, Johann M. Rohwer, Johannes Eichner, Daniel R. Hyduke, James R. Faeder, Stefan Hoops, Emanuel Gonçalves, Yuichiro Inagaki, Aurélien Naldi, Koichi Takahashi, Sylvain Soliman, Brett G. Olivier, Kieran Smallbone, Stuart L. Moodie, Pedro T. Monteiro, Chris J. Myers, Martin Golebiewski, Tomas Radivoyevitch, Jeremy Zucker, Hidde de Jong, Andrew Finney, Keating, S, Waltemath, D, König, M, Zhang, F, Dräger, A, Chaouiya, C, Bergmann, F, Finney, A, Gillespie, C, Helikar, T, Hoops, S, Malik-Sheriff, R, Moodie, S, Moraru, I, Myers, C, Naldi, A, Olivier, B, Sahle, S, Schaff, J, Smith, L, Swat, M, Thieffry, D, Watanabe, L, Wilkinson, D, Blinov, M, Begley, K, Faeder, J, Gómez, H, Hamm, T, Inagaki, Y, Liebermeister, W, Lister, A, Lucio, D, Mjolsness, E, Proctor, C, Raman, K, Rodriguez, N, Shaffer, C, Shapiro, B, Stelling, J, Swainston, N, Tanimura, N, Wagner, J, Meier-Schellersheim, M, Sauro, H, Palsson, B, Bolouri, H, Kitano, H, Funahashi, A, Hermjakob, H, Doyle, J, Hucka, M, Adams, R, Allen, N, Angermann, B, Antoniotti, M, Bader, G, Červený, J, Courtot, M, Cox, C, Dalle Pezze, P, Demir, E, Denney, W, Dharuri, H, Dorier, J, Drasdo, D, Ebrahim, A, Eichner, J, Elf, J, Endler, L, Evelo, C, Flamm, C, Fleming, R, Fröhlich, M, Glont, M, Gonçalves, E, Golebiewski, M, Grabski, H, Gutteridge, A, Hachmeister, D, Harris, L, Heavner, B, Henkel, R, Hlavacek, W, Hu, B, Hyduke, D, Jong, H, Juty, N, Karp, P, Karr, J, Kell, D, Keller, R, Kiselev, I, Klamt, S, Klipp, E, Knüpfer, C, Kolpakov, F, Krause, F, Kutmon, M, Laibe, C, Lawless, C, Li, L, Loew, L, Machne, R, Matsuoka, Y, Mendes, P, Mi, H, Mittag, F, Monteiro, P, Natarajan, K, Nielsen, P, Nguyen, T, Palmisano, A, Jean-Baptiste, P, Pfau, T, Phair, R, Radivoyevitch, T, Rohwer, J, Ruebenacker, O, Saez-Rodriguez, J, Scharm, M, Schmidt, H, Schreiber, F, Schubert, M, Schulte, R, Sealfon, S, Smallbone, K, Soliman, S, Stefan, M, Sullivan, D, Takahashi, K, Teusink, B, Tolnay, D, Vazirabad, I, Kamp, A, Wittig, U, Wrzodek, C, Wrzodek, F, Xenarios, I, Zhukova, A, Zucker, J, European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, Heidelberg University Hospital [Heidelberg], Swiss Institute of Bioinformatics [Lausanne] (SIB), Université de Lausanne = University of Lausanne (UNIL), European Molecular Biology Laboratory (EMBL), University of Connecticut (UCONN), National Institutes of Health [Bethesda] (NIH), Chercheur indépendant, Amazon Web Services [Seattle] (AWS), Università degli Studi di Milano-Bicocca = University of Milano-Bicocca (UNIMIB), University of Toronto, Masaryk University [Brno] (MUNI), Terry Fox Laboratory, BC Cancer Agency (BCCRC)-British Columbia Cancer Agency Research Centre, The University of Tennessee [Knoxville], The Babraham Institute [Cambridge, UK], Oregon Health and Science University [Portland] (OHSU), Human Predictions LLC, Illumina, Swiss-Prot Group, Swiss Institute of Bioinformatics [Genève] (SIB), Modelling and Analysis for Medical and Biological Applications (MAMBA), Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Laboratoire Jacques-Louis Lions (LJLL (UMR_7598)), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), University of California [San Diego] (UC San Diego), University of California (UC), Center for Bioinformatics (ZBIT), Eberhard Karls Universität Tübingen = Eberhard Karls University of Tuebingen, Uppsala University, Institut für Populationsgenetik [Vienna], Veterinärmedizinische Universität Wien, Maastricht University [Maastricht], Alpen-Adria-Universität Klagenfurt [Klagenfurt, Austria], Medizinische Universität Wien = Medical University of Vienna, German Cancer Research Center - Deutsches Krebsforschungszentrum [Heidelberg] (DKFZ), Heidelberg Institute for Theoretical Studies (HITS ), Russian-Armenian University (RAU), GlaxoSmithKline [Stevenage, UK] (GSK), GlaxoSmithKline [Headquarters, London, UK] (GSK), Microsoft Technology Licensing (MTL), Microsoft Corporation [Redmond, Wash.], Vanderbilt University School of Medicine [Nashville], University of Washington [Seattle], University of Rostock, Los Alamos National Laboratory (LANL), Lorentz Institute, Universiteit Leiden, Tegmine Therapeutics, Modeling, simulation, measurement, and control of bacterial regulatory networks (IBIS), Laboratoire Adaptation et pathogénie des micro-organismes [Grenoble] (LAPM), Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut Jean Roget, SRI International [Menlo Park] (SRI), Icahn School of Medicine at Mount Sinai [New York] (MSSM), University of Liverpool, Universitätsklinikum Tübingen - University Hospital of Tübingen, Institute of Information and Computational Technologies (IICT), Max Planck Institute for Dynamics of Complex Technical Systems, Max-Planck-Gesellschaft, Max-Planck-Institut für Molekulare Genetik (MPIMG), Friedrich-Schiller-Universität = Friedrich Schiller University Jena [Jena, Germany], Humboldt University Of Berlin, Newcastle University [Newcastle], École polytechnique (X), Heinrich Heine Universität Düsseldorf = Heinrich Heine University [Düsseldorf], The Systems Biology Institute [Tokyo] (SBI), Centro de Quimica Estrutural (CQE), Instituto Superior Técnico, Universidade Técnica de Lisboa (IST), University of Southern California (USC), Instituto Gulbenkian de Ciência [Oeiras] (IGC), Fundação Calouste Gulbenkian, University of Southern Denmark (SDU), University of Auckland [Auckland], University of Utah, Virginia Tech [Blacksburg], University of Luxembourg [Luxembourg], Integrative Bioinformatics Inc [Mountain View], Cleveland Clinic, Stellenbosch University, Broad Institute of MIT and Harvard (BROAD INSTITUTE), Harvard Medical School [Boston] (HMS)-Massachusetts Institute of Technology (MIT)-Massachusetts General Hospital [Boston], Universität Heidelberg [Heidelberg] = Heidelberg University, Leibniz Institute of Plant Genetics and Crop Plant Research [Gatersleben] (IPK-Gatersleben), Laboratoire de Biologie du Développement de Villefranche sur mer (LBDV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Mount Sinai School of Medicine, Department of Psychiatry-Icahn School of Medicine at Mount Sinai [New York] (MSSM), University of Manchester [Manchester], Computational systems biology and optimization (Lifeware), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), California Institute of Technology (CALTECH), Encodia Inc [San Diego], Shinshu University [Nagano], University of Amsterdam [Amsterdam] (UvA), Versiti Blood Center of Wisconsin, Greifswald University Hospital, Bioinformatique évolutive - Evolutionary Bioinformatics, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS), Pacific Northwest National Laboratory (PNNL), National Institute of Allergy and Infectious Diseases [Bethesda] (NIAID-NIH), Department of Bioengineering, University of California (UC)-University of California (UC), ANSYS, Virginia Polytechnic Institute and State University [Blacksburg], Eight Pillars Ltd, Center for Integrative Genomics - Institute of Bioinformatics, Génopode (CIG), Université de Lausanne = University of Lausanne (UNIL)-Université de Lausanne = University of Lausanne (UNIL), Universität Heidelberg, Bioquant, Applied Biomathematics [New York], SimCYP Ltd, Institut de biologie de l'ENS Paris (IBENS), Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), University of Utah School of Medicine [Salt Lake City], University of Pittsburgh School of Medicine, Pennsylvania Commonwealth System of Higher Education (PCSHE), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Mizuho Information and Research Institute, Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] (MaIAGE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), University of Oxford, Computer Science (North Carolina State University), North Carolina State University [Raleigh] (NC State), University of North Carolina System (UNC)-University of North Carolina System (UNC), University of California [Irvine] (UC Irvine), Indian Institute of Technology Madras (IIT Madras), California State University [Northridge] (CSUN), Biotechnology and Biological Sciences Research Council (BBSRC), IBM Research [Melbourne], Benaroya Research Institute [Seattle] (BRI), Okinawa Institute of Science and Technology Graduate University, Keio University, Department of Computing and Mathematical sciences, members, SBML Level 3 Community, Université de Lausanne (UNIL), Università degli Studi di Milano-Bicocca [Milano] (UNIMIB), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP), University of California, Universiteit Leiden [Leiden], Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Inria Grenoble - Rhône-Alpes, Humboldt University of Berlin, Universität Heidelberg [Heidelberg], Institut Pasteur [Paris]-Centre National de la Recherche Scientifique (CNRS), Humboldt-Universität zu Berlin, University of California-University of California, Université de Lausanne (UNIL)-Université de Lausanne (UNIL), Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Département de Biologie - ENS Paris, École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), University of Oxford [Oxford], University of California [Irvine] (UCI), Biotechnology and Biological Sciences Research Council, Computer Science, Institut de biologie de l'ENS Paris (UMR 8197/1024) (IBENS), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS)-École normale supérieure - Paris (ENS Paris)
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computational modeling ,Medicine (General) ,Markup language ,[SDV.BIO]Life Sciences [q-bio]/Biotechnology ,INFORMATION ,Interoperability ,interoperability ,Review ,[SDV.BC.BC]Life Sciences [q-bio]/Cellular Biology/Subcellular Processes [q-bio.SC] ,ANNOTATION ,0302 clinical medicine ,Software ,file forma ,Models ,Biology (General) ,0303 health sciences ,Computational model ,Applied Mathematics ,Systems Biology ,systems biology ,File format ,3. Good health ,Networking and Information Technology R&D ,Networking and Information Technology R&D (NITRD) ,Computational Theory and Mathematics ,SIMULATION ,General Agricultural and Biological Sciences ,STANDARDS ,REPOSITORY ,Information Systems ,QH301-705.5 ,Bioinformatics ,Systems biology ,Software ecosystem ,Reviews ,Bioengineering ,Methods & Resources ,Biology ,MARKUP LANGUAGE ,Models, Biological ,SBML Level 3 Community members ,General Biochemistry, Genetics and Molecular Biology ,03 medical and health sciences ,R5-920 ,Animals ,Humans ,SBML ,reproducibility ,030304 developmental biology ,ENVIRONMENT ,General Immunology and Microbiology ,file format ,business.industry ,Computational Biology ,Biological ,ONTOLOGY ,Metabolism ,Logistic Models ,Biochemistry and Cell Biology ,Other Biological Sciences ,Software engineering ,business ,030217 neurology & neurosurgery - Abstract
Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution., Over the past two decades, scientists from different fields have been developing SBML, a standard format for encoding computational models in biology and medicine. This article summarizes recent progress and gives perspectives on emerging challenges.
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- 2020
37. Provenance Information for Biomedical Data and Workflows: Scoping Review.
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Gierend K, Krüger F, Genehr S, Hartmann F, Siegel F, Waltemath D, Ganslandt T, and Zeleke AA
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- Humans, Biomedical Research methods, Workflow
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Background: The record of the origin and the history of data, known as provenance, holds importance. Provenance information leads to higher interpretability of scientific results and enables reliable collaboration and data sharing. However, the lack of comprehensive evidence on provenance approaches hinders the uptake of good scientific practice in clinical research., Objective: This scoping review aims to identify approaches and criteria for provenance tracking in the biomedical domain. We reviewed the state-of-the-art frameworks, associated artifacts, and methodologies for provenance tracking., Methods: This scoping review followed the methodological framework developed by Arksey and O'Malley. We searched the PubMed and Web of Science databases for English-language articles published from 2006 to 2022. Title and abstract screening were carried out by 4 independent reviewers using the Rayyan screening tool. A majority vote was required for consent on the eligibility of papers based on the defined inclusion and exclusion criteria. Full-text reading and screening were performed independently by 2 reviewers, and information was extracted into a pretested template for the 5 research questions. Disagreements were resolved by a domain expert. The study protocol has previously been published., Results: The search resulted in a total of 764 papers. Of 624 identified, deduplicated papers, 66 (10.6%) studies fulfilled the inclusion criteria. We identified diverse provenance-tracking approaches ranging from practical provenance processing and managing to theoretical frameworks distinguishing diverse concepts and details of data and metadata models, provenance components, and notations. A substantial majority investigated underlying requirements to varying extents and validation intensities but lacked completeness in provenance coverage. Mostly, cited requirements concerned the knowledge about data integrity and reproducibility. Moreover, these revolved around robust data quality assessments, consistent policies for sensitive data protection, improved user interfaces, and automated ontology development. We found that different stakeholder groups benefit from the availability of provenance information. Thereby, we recognized that the term provenance is subjected to an evolutionary and technical process with multifaceted meanings and roles. Challenges included organizational and technical issues linked to data annotation, provenance modeling, and performance, amplified by subsequent matters such as enhanced provenance information and quality principles., Conclusions: As data volumes grow and computing power increases, the challenge of scaling provenance systems to handle data efficiently and assist complex queries intensifies, necessitating automated and scalable solutions. With rising legal and scientific demands, there is an urgent need for greater transparency in implementing provenance systems in research projects, despite the challenges of unresolved granularity and knowledge bottlenecks. We believe that our recommendations enable quality and guide the implementation of auditable and measurable provenance approaches as well as solutions in the daily tasks of biomedical scientists., International Registered Report Identifier (irrid): RR2-10.2196/31750., (©Kerstin Gierend, Frank Krüger, Sascha Genehr, Francisca Hartmann, Fabian Siegel, Dagmar Waltemath, Thomas Ganslandt, Atinkut Alamirrew Zeleke. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 23.08.2024.)
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- 2024
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38. MeDaX: A Knowledge Graph on FHIR.
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Mazein I, Gebhardt T, Zinkewitz F, Michaelis L, Braun S, Waltemath D, Henkel R, and Wodke JAH
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- Humans, Health Level Seven, Germany, Databases, Factual, Electronic Health Records
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In Germany, the standard format for exchange of clinical care data for research is HL7 FHIR. Graph databases (GDBs), well suited for integrating complex and heterogeneous data from diverse sources, are currently gaining traction in the medical field. They provide a versatile framework for data analysis which is generally challenging for raw FHIR-formatted data. For generation of a knowledge graph (KG) for clinical research data, we tested different extract-transform-load (ETL) approaches to convert FHIR into graph format. We designed a generalised ETL process and implemented a prototypic pipeline for automated KG creation and ontological structuring. The MeDaX-KG prototype is built from synthetic patient data and currently serves internal testing purposes. The presented approach is easy to customise to expand to other data types and formats.
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- 2024
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39. Investigating the neural mechanisms of transcranial direct current stimulation effects on human cognition: current issues and potential solutions.
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Meinzer M, Shahbabaie A, Antonenko D, Blankenburg F, Fischer R, Hartwigsen G, Nitsche MA, Li SC, Thielscher A, Timmann D, Waltemath D, Abdelmotaleb M, Kocataş H, Caisachana Guevara LM, Batsikadze G, Grundei M, Cunha T, Hayek D, Turker S, Schlitt F, Shi Y, Khan A, Burke M, Riemann S, Niemann F, and Flöel A
- Abstract
Transcranial direct current stimulation (tDCS) has been studied extensively for its potential to enhance human cognitive functions in healthy individuals and to treat cognitive impairment in various clinical populations. However, little is known about how tDCS modulates the neural networks supporting cognition and the complex interplay with mediating factors that may explain the frequently observed variability of stimulation effects within and between studies. Moreover, research in this field has been characterized by substantial methodological variability, frequent lack of rigorous experimental control and small sample sizes, thereby limiting the generalizability of findings and translational potential of tDCS. The present manuscript aims to delineate how these important issues can be addressed within a neuroimaging context, to reveal the neural underpinnings, predictors and mediators of tDCS-induced behavioral modulation. We will focus on functional magnetic resonance imaging (fMRI), because it allows the investigation of tDCS effects with excellent spatial precision and sufficient temporal resolution across the entire brain. Moreover, high resolution structural imaging data can be acquired for precise localization of stimulation effects, verification of electrode positions on the scalp and realistic current modeling based on individual head and brain anatomy. However, the general principles outlined in this review will also be applicable to other imaging modalities. Following an introduction to the overall state-of-the-art in this field, we will discuss in more detail the underlying causes of variability in previous tDCS studies. Moreover, we will elaborate on design considerations for tDCS-fMRI studies, optimization of tDCS and imaging protocols and how to assure high-level experimental control. Two additional sections address the pressing need for more systematic investigation of tDCS effects across the healthy human lifespan and implications for tDCS studies in age-associated disease, and potential benefits of establishing large-scale, multidisciplinary consortia for more coordinated tDCS research in the future. We hope that this review will contribute to more coordinated, methodologically sound, transparent and reproducible research in this field. Ultimately, our aim is to facilitate a better understanding of the underlying mechanisms by which tDCS modulates human cognitive functions and more effective and individually tailored translational and clinical applications of this technique in the future., Competing Interests: MAN is in the scientific advisory board of Neuroelectrics and Précis’s. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer BL declared a past co-authorship with the authors AF and MAN to the handling editor., (Copyright © 2024 Meinzer, Shahbabaie, Antonenko, Blankenburg, Fischer, Hartwigsen, Nitsche, Li, Thielscher, Timmann, Waltemath, Abdelmotaleb, Kocataş, Caisachana Guevara, Batsikadze, Grundei, Cunha, Hayek, Turker, Schlitt, Shi, Khan, Burke, Riemann, Niemann and Flöel.)
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- 2024
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40. [FAIR health data in the national and international data space].
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Waltemath D, Beyan O, Crameri K, Dedié A, Gierend K, Gröber P, Inau ET, Michaelis L, Reinecke I, Sedlmayr M, Thun S, and Krefting D
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- Humans, Germany, Internationality, National Health Programs, Electronic Health Records
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Health data are extremely important in today's data-driven world. Through automation, healthcare processes can be optimized, and clinical decisions can be supported. For any reuse of data, the quality, validity, and trustworthiness of data are essential, and it is the only way to guarantee that data can be reused sensibly. Specific requirements for the description and coding of reusable data are defined in the FAIR guiding principles for data stewardship. Various national research associations and infrastructure projects in the German healthcare sector have already clearly positioned themselves on the FAIR principles: both the infrastructures of the Medical Informatics Initiative and the University Medicine Network operate explicitly on the basis of the FAIR principles, as do the National Research Data Infrastructure for Personal Health Data and the German Center for Diabetes Research.To ensure that a resource complies with the FAIR principles, the degree of FAIRness should first be determined (so-called FAIR assessment), followed by the prioritization for improvement steps (so-called FAIRification). Since 2016, a set of tools and guidelines have been developed for both steps, based on the different, domain-specific interpretations of the FAIR principles.Neighboring European countries have also invested in the development of a national framework for semantic interoperability in the context of the FAIR (Findable, Accessible, Interoperable, Reusable) principles. Concepts for comprehensive data enrichment were developed to simplify data analysis, for example, in the European Health Data Space or via the Observational Health Data Sciences and Informatics network. With the support of the European Open Science Cloud, among others, structured FAIRification measures have already been taken for German health datasets., (© 2024. The Author(s).)
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- 2024
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41. Herding Cats in Pandemic Times - Towards Technological and Organizational Convergence of Heterogeneous Solutions for Investigating and Mastering the Pandemic in University Medical Centers.
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Krefting D, Mutters NT, Pryss R, Sedlmayr M, Boeker M, Dieterich C, Koll C, Mueller M, Slagman A, Waltemath D, Wulf A, and Zenker S
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- Humans, Academic Medical Centers, Health Facilities, Pandemics, Biomedical Research, COVID-19 epidemiology
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To understand and handle the COVID-19 pandemic, digital tools and infrastructures were built in very short timeframes, resulting in stand-alone and non-interoperable solutions. To shape an interoperable, sustainable, and extensible ecosystem to advance biomedical research and healthcare during the pandemic and beyond, a short-term project called "Collaborative Data Exchange and Usage" (CODEX+) was initiated to integrate and connect multiple COVID-19 projects into a common organizational and technical framework. In this paper, we present the conceptual design, provide an overview of the results, and discuss the impact of such a project for the trade-off between innovation and sustainable infrastructures.
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- 2024
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42. Navigating the AI frontiers in cardiovascular research: a bibliometric exploration and topic modeling.
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Shiferaw KB, Wali P, Waltemath D, and Zeleke AA
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Artificial intelligence (AI) has emerged as a promising field in cardiovascular disease (CVD) research, offering innovative approaches to enhance diagnosis, treatment, and patient outcomes. In this study, we conducted bibliometric analysis combined with topic modeling to provide a comprehensive overview of the AI research landscape in CVD. Our analysis included 23,846 studies from Web of Science and PubMed, capturing the latest advancements and trends in this rapidly evolving field. By employing LDA (Latent Dirichlet Allocation) we identified key research themes, trends, and collaborations within the AI-CVD domain. The findings revealed the exponential growth of AI-related research in CVD, underscoring its immense potential to revolutionize cardiovascular healthcare. The annual scientific publication of machine learning papers in CVD increases continuously and significantly since 2016, with an overall annual growth rate of 22.8%. Almost half (46.2%) of the growth happened in the last 5 years. USA, China, India, UK and Korea were the top five productive countries in number of publications. UK, Germany and Australia were the most collaborative countries with a multiple country publication (MCP) value of 42.8%, 40.3% and 40.0% respectively. We observed the emergence of twenty-two distinct research topics, including "stroke and robotic rehabilitation therapy," "robotic-assisted cardiac surgery," and "cardiac image analysis," which persisted as major topics throughout the years. Other topics, such as "retinal image analysis and CVD" and "biomarker and wearable signal analyses," have recently emerged as dominant areas of research in cardiovascular medicine. Convolutional neural network appears to be the most mentioned algorithm followed by LSTM (Long Short-Term Memory) and KNN (K-Nearest Neighbours). This indicates that the future direction of AI cardiovascular research is predominantly directing toward neural networks and image analysis. As AI continues to shape the landscape of CVD research, our study serves as a comprehensive guide for researchers, practitioners, and policymakers, providing valuable insights into the current state of AI in CVD research. This study offers a deep understanding of research trends and paves the way for future directions to maximiz the potential of AI to effectively combat cardiovascular diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Shiferaw, Wali, Waltemath and Zeleke.)
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- 2024
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43. Traceable Research Data Sharing in a German Medical Data Integration Center With FAIR (Findability, Accessibility, Interoperability, and Reusability)-Geared Provenance Implementation: Proof-of-Concept Study.
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Gierend K, Waltemath D, Ganslandt T, and Siegel F
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Background: Secondary investigations into digital health records, including electronic patient data from German medical data integration centers (DICs), pave the way for enhanced future patient care. However, only limited information is captured regarding the integrity, traceability, and quality of the (sensitive) data elements. This lack of detail diminishes trust in the validity of the collected data. From a technical standpoint, adhering to the widely accepted FAIR (Findability, Accessibility, Interoperability, and Reusability) principles for data stewardship necessitates enriching data with provenance-related metadata. Provenance offers insights into the readiness for the reuse of a data element and serves as a supplier of data governance., Objective: The primary goal of this study is to augment the reusability of clinical routine data within a medical DIC for secondary utilization in clinical research. Our aim is to establish provenance traces that underpin the status of data integrity, reliability, and consequently, trust in electronic health records, thereby enhancing the accountability of the medical DIC. We present the implementation of a proof-of-concept provenance library integrating international standards as an initial step., Methods: We adhered to a customized road map for a provenance framework, and examined the data integration steps across the ETL (extract, transform, and load) phases. Following a maturity model, we derived requirements for a provenance library. Using this research approach, we formulated a provenance model with associated metadata and implemented a proof-of-concept provenance class. Furthermore, we seamlessly incorporated the internationally recognized Word Wide Web Consortium (W3C) provenance standard, aligned the resultant provenance records with the interoperable health care standard Fast Healthcare Interoperability Resources, and presented them in various representation formats. Ultimately, we conducted a thorough assessment of provenance trace measurements., Results: This study marks the inaugural implementation of integrated provenance traces at the data element level within a German medical DIC. We devised and executed a practical method that synergizes the robustness of quality- and health standard-guided (meta)data management practices. Our measurements indicate commendable pipeline execution times, attaining notable levels of accuracy and reliability in processing clinical routine data, thereby ensuring accountability in the medical DIC. These findings should inspire the development of additional tools aimed at providing evidence-based and reliable electronic health record services for secondary use., Conclusions: The research method outlined for the proof-of-concept provenance class has been crafted to promote effective and reliable core data management practices. It aims to enhance biomedical data by imbuing it with meaningful provenance, thereby bolstering the benefits for both research and society. Additionally, it facilitates the streamlined reuse of biomedical data. As a result, the system mitigates risks, as data analysis without knowledge of the origin and quality of all data elements is rendered futile. While the approach was initially developed for the medical DIC use case, these principles can be universally applied throughout the scientific domain., (©Kerstin Gierend, Dagmar Waltemath, Thomas Ganslandt, Fabian Siegel. Originally published in JMIR Formative Research (https://formative.jmir.org), 07.12.2023.)
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- 2023
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44. The Status of Data Management Practices Across German Medical Data Integration Centers: Mixed Methods Study.
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Gierend K, Freiesleben S, Kadioglu D, Siegel F, Ganslandt T, and Waltemath D
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- Humans, Delivery of Health Care, Surveys and Questionnaires, Data Management, Medical Informatics
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Background: In the context of the Medical Informatics Initiative, medical data integration centers (DICs) have implemented complex data flows to transfer routine health care data into research data repositories for secondary use. Data management practices are of importance throughout these processes, and special attention should be given to provenance aspects. Insufficient knowledge can lead to validity risks and reduce the confidence and quality of the processed data. The need to implement maintainable data management practices is undisputed, but there is a great lack of clarity on the status., Objective: Our study examines the current data management practices throughout the data life cycle within the Medical Informatics in Research and Care in University Medicine (MIRACUM) consortium. We present a framework for the maturity status of data management practices and present recommendations to enable a trustful dissemination and reuse of routine health care data., Methods: In this mixed methods study, we conducted semistructured interviews with stakeholders from 10 DICs between July and September 2021. We used a self-designed questionnaire that we tailored to the MIRACUM DICs, to collect qualitative and quantitative data. Our study method is compliant with the Good Reporting of a Mixed Methods Study (GRAMMS) checklist., Results: Our study provides insights into the data management practices at the MIRACUM DICs. We identify several traceability issues that can be partially explained with a lack of contextual information within nonharmonized workflow steps, unclear responsibilities, missing or incomplete data elements, and incomplete information about the computational environment information. Based on the identified shortcomings, we suggest a data management maturity framework to reach more clarity and to help define enhanced data management strategies., Conclusions: The data management maturity framework supports the production and dissemination of accurate and provenance-enriched data for secondary use. Our work serves as a catalyst for the derivation of an overarching data management strategy, abiding data integrity and provenance characteristics as key factors. We envision that this work will lead to the generation of fairer and maintained health research data of high quality., (©Kerstin Gierend, Sherry Freiesleben, Dennis Kadioglu, Fabian Siegel, Thomas Ganslandt, Dagmar Waltemath. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 08.11.2023.)
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- 2023
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45. Guidelines and Standard Frameworks for AI in Medicine: Protocol for a Systematic Literature Review.
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Shiferaw KB, Roloff M, Waltemath D, and Zeleke AA
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Background: Applications of artificial intelligence (AI) are pervasive in modern biomedical science. In fact, research results suggesting algorithms and AI models for different target diseases and conditions are continuously increasing. While this situation undoubtedly improves the outcome of AI models, health care providers are increasingly unsure which AI model to use due to multiple alternatives for a specific target and the "black box" nature of AI. Moreover, the fact that studies rarely use guidelines in developing and reporting AI models poses additional challenges in trusting and adapting models for practical implementation., Objective: This review protocol describes the planned steps and methods for a review of the synthesized evidence regarding the quality of available guidelines and frameworks to facilitate AI applications in medicine., Methods: We will commence a systematic literature search using medical subject headings terms for medicine, guidelines, and machine learning (ML). All available guidelines, standard frameworks, best practices, checklists, and recommendations will be included, irrespective of the study design. The search will be conducted on web-based repositories such as PubMed, Web of Science, and the EQUATOR (Enhancing the Quality and Transparency of Health Research) network. After removing duplicate results, a preliminary scan for titles will be done by 2 reviewers. After the first scan, the reviewers will rescan the selected literature for abstract review, and any incongruities about whether to include the article for full-text review or not will be resolved by the third and fourth reviewer based on the predefined criteria. A Google Scholar (Google LLC) search will also be performed to identify gray literature. The quality of identified guidelines will be evaluated using the Appraisal of Guidelines, Research, and Evaluation (AGREE II) tool. A descriptive summary and narrative synthesis will be carried out, and the details of critical appraisal and subgroup synthesis findings will be presented., Results: The results will be reported using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) reporting guidelines. Data analysis is currently underway, and we anticipate finalizing the review by November 2023., Conclusions: Guidelines and recommended frameworks for developing, reporting, and implementing AI studies have been developed by different experts to facilitate the reliable assessment of validity and consistent interpretation of ML models for medical applications. We postulate that a guideline supports the assessment of an ML model only if the quality and reliability of the guideline are high. Assessing the quality and aspects of available guidelines, recommendations, checklists, and frameworks-as will be done in the proposed review-will provide comprehensive insights into current gaps and help to formulate future research directions., International Registered Report Identifier (irrid): DERR1-10.2196/47105., (©Kirubel Biruk Shiferaw, Moritz Roloff, Dagmar Waltemath, Atinkut Alamirrew Zeleke. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 25.10.2023.)
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- 2023
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46. Initiatives, Concepts, and Implementation Practices of the Findable, Accessible, Interoperable, and Reusable Data Principles in Health Data Stewardship: Scoping Review.
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Inau ET, Sack J, Waltemath D, and Zeleke AA
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- Humans, Pandemics, Big Data, Data Accuracy, COVID-19, Cardiovascular Diseases
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Background: Thorough data stewardship is a key enabler of comprehensive health research. Processes such as data collection, storage, access, sharing, and analytics require researchers to follow elaborate data management strategies properly and consistently. Studies have shown that findable, accessible, interoperable, and reusable (FAIR) data leads to improved data sharing in different scientific domains., Objective: This scoping review identifies and discusses concepts, approaches, implementation experiences, and lessons learned in FAIR initiatives in health research data., Methods: The Arksey and O'Malley stage-based methodological framework for scoping reviews was applied. PubMed, Web of Science, and Google Scholar were searched to access relevant publications. Articles written in English, published between 2014 and 2020, and addressing FAIR concepts or practices in the health domain were included. The 3 data sources were deduplicated using a reference management software. In total, 2 independent authors reviewed the eligibility of each article based on defined inclusion and exclusion criteria. A charting tool was used to extract information from the full-text papers. The results were reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines., Results: A total of 2.18% (34/1561) of the screened articles were included in the final review. The authors reported FAIRification approaches, which include interpolation, inclusion of comprehensive data dictionaries, repository design, semantic interoperability, ontologies, data quality, linked data, and requirement gathering for FAIRification tools. Challenges and mitigation strategies associated with FAIRification, such as high setup costs, data politics, technical and administrative issues, privacy concerns, and difficulties encountered in sharing health data despite its sensitive nature were also reported. We found various workflows, tools, and infrastructures designed by different groups worldwide to facilitate the FAIRification of health research data. We also uncovered a wide range of problems and questions that researchers are trying to address by using the different workflows, tools, and infrastructures. Although the concept of FAIR data stewardship in the health research domain is relatively new, almost all continents have been reached by at least one network trying to achieve health data FAIRness. Documented outcomes of FAIRification efforts include peer-reviewed publications, improved data sharing, facilitated data reuse, return on investment, and new treatments. Successful FAIRification of data has informed the management and prognosis of various diseases such as cancer, cardiovascular diseases, and neurological diseases. Efforts to FAIRify data on a wider variety of diseases have been ongoing since the COVID-19 pandemic., Conclusions: This work summarises projects, tools, and workflows for the FAIRification of health research data. The comprehensive review shows that implementing the FAIR concept in health data stewardship carries the promise of improved research data management and transparency in the era of big data and open research publishing., International Registered Report Identifier (irrid): RR2-10.2196/22505., (©Esther Thea Inau, Jean Sack, Dagmar Waltemath, Atinkut Alamirrew Zeleke. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 28.08.2023.)
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- 2023
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47. Democratizing knowledge representation with BioCypher.
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Lobentanzer S, Aloy P, Baumbach J, Bohar B, Carey VJ, Charoentong P, Danhauser K, Doğan T, Dreo J, Dunham I, Farr E, Fernandez-Torras A, Gyori BM, Hartung M, Hoyt CT, Klein C, Korcsmaros T, Maier A, Mann M, Ochoa D, Pareja-Lorente E, Popp F, Preusse M, Probul N, Schwikowski B, Sen B, Strauss MT, Turei D, Ulusoy E, Waltemath D, Wodke JAH, and Saez-Rodriguez J
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- 2023
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48. Ten Topics to Get Started in Medical Informatics Research.
- Author
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Wolfien M, Ahmadi N, Fitzer K, Grummt S, Heine KL, Jung IC, Krefting D, Kühn A, Peng Y, Reinecke I, Scheel J, Schmidt T, Schmücker P, Schüttler C, Waltemath D, Zoch M, and Sedlmayr M
- Subjects
- Humans, Curriculum, Algorithms, Germany, Medical Informatics
- Abstract
The vast and heterogeneous data being constantly generated in clinics can provide great wealth for patients and research alike. The quickly evolving field of medical informatics research has contributed numerous concepts, algorithms, and standards to facilitate this development. However, these difficult relationships, complex terminologies, and multiple implementations can present obstacles for people who want to get active in the field. With a particular focus on medical informatics research conducted in Germany, we present in our Viewpoint a set of 10 important topics to improve the overall interdisciplinary communication between different stakeholders (eg, physicians, computational experts, experimentalists, students, patient representatives). This may lower the barriers to entry and offer a starting point for collaborations at different levels. The suggested topics are briefly introduced, then general best practice guidance is given, and further resources for in-depth reading or hands-on tutorials are recommended. In addition, the topics are set to cover current aspects and open research gaps of the medical informatics domain, including data regulations and concepts; data harmonization and processing; and data evaluation, visualization, and dissemination. In addition, we give an example on how these topics can be integrated in a medical informatics curriculum for higher education. By recognizing these topics, readers will be able to (1) set clinical and research data into the context of medical informatics, understanding what is possible to achieve with data or how data should be handled in terms of data privacy and storage; (2) distinguish current interoperability standards and obtain first insights into the processes leading to effective data transfer and analysis; and (3) value the use of newly developed technical approaches to utilize the full potential of clinical data., (©Markus Wolfien, Najia Ahmadi, Kai Fitzer, Sophia Grummt, Kilian-Ludwig Heine, Ian-C Jung, Dagmar Krefting, Andreas Kühn, Yuan Peng, Ines Reinecke, Julia Scheel, Tobias Schmidt, Paul Schmücker, Christina Schüttler, Dagmar Waltemath, Michele Zoch, Martin Sedlmayr. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 24.07.2023.)
- Published
- 2023
- Full Text
- View/download PDF
49. Identifying Relevant FHIR Elements for Data Quality Assessment in the German Core Data Set.
- Author
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Draeger C, Tute E, Schmidt CO, Waltemath D, Boeker M, Winter A, and Löbe M
- Subjects
- Humans, Electronic Health Records, Data Accuracy, Hospitals, University, Medical Informatics, Biomedical Research
- Abstract
The German Medical Informatics Initiative makes clinical routine data available for biomedical research. In total, 37 university hospitals have set up so-called data integration centers to facilitate this data reuse. A standardized set of HL7 FHIR profiles ("MII Core Data Set") defines the common data model across all centers. Regular Projectathons ensure continuous evaluation of the implemented data sharing processes on artificial and real-world clinical use cases. In this context, FHIR continues to rise in popularity for exchanging patient care data. As reusing data from patient care in clinical research requires high trust in the data, data quality assessments are a key point of concern in the data sharing process. To support the setup of data quality assessments within data integration centers, we suggest a process for finding elements of interest from FHIR profiles. We focus on the specific data quality measures defined by Kahn et al.
- Published
- 2023
- Full Text
- View/download PDF
50. Exploring New Possibilities for Research Data Exploration Using the Example of the German Core Data Set.
- Author
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Menzel F, Waltemath D, and Henkel R
- Subjects
- Information Dissemination, Data Warehousing, Databases, Factual, Health Level Seven, Electronic Health Records, Information Storage and Retrieval
- Abstract
The German Medical Informatics Initiative (MII) aims to increase the interoperability and reuse of clinical routine data for research purposes. One important result of the MII work is a German-wide common core data set (CDS), which is to be provided by over 31 data integration centers (DIZ) following a strict specification. One standard format for data sharing is HL7/FHIR. Locally, classical data warehouses are often in use for data storage and retrieval. We are interested to investigate the advantages of a graph database in this setting. After having transferred the MII CDS into a graph, storing it in a graph database and subsequently enriching it with accompanying meta-information, we see a great potential for more sophisticated data exploration and analysis. Here we describe the extract-transform-load process which we set up as a proof of concept to achieve the transformation and to make the common set of core data accessible as a graph.
- Published
- 2023
- Full Text
- View/download PDF
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