Filter and export

Publications

What is a Publication?
104 Publications visible to you, out of a total of 104
Publication Policy of the National Research Data Infrastructure for Personal Health Data (NFDI4Health) and the NFDI4Health Task Force COVID-19

Abstract

Not specified

Authors: Birte Lindstädt, Aliaksandra Shutsko, Martin Golebiewski, Dennis-Kenji Kipker, Vanessa Lettieri, Sophie Klopfenstein, Carina Vorisek, Matthias Löbe, Carsten Oliver Schmidt

Date Published: 11th Feb 2022

Publication Type: Manual

White Paper: Towards in silico approaches for personalised medicine – Recommendations for verifying and validating predictive computational models in EU collaborative research

Abstract (Expand)

Harmonization of data integration is the key to standardization efforts in personalised medicine, which would also facilitate cross-European studies. Standardization of the models themselves is less … essential within a research context, where new models are created and tested in line with research progress, harmonization and/or standardization of input data is both feasible and necessary. We argue that model validation should receive more attention, and other measures should be implemented such that validation of models within personalised medicine becomes easier, also across borders. While this is an evident necessity within the context of models implemented as medical devices or decision tools, which are regulated by the European Medicines Agency and national competent authorities, we argue that model validation should be a higher priority at research level also, facilitating assessment by peers and by medical doctors – who themselves should receive better training in assessment of research using in silico models. This will also ease the implementation of translational research results in the clinic. Acceptance by doctors and the relevant medical specialties is a key hurdle for in silico models in personalised medicine. Any medical product - device, algorithm or drug - has to prove itself safe and effective to be licensed for use by regulators; however, it has also to be accepted by medical experts as being a good choice, and be recommended within clinical specialties. EU-STANDS4PM joined forces to examine to what extent existing standards or standards under development for both format and semantics can be used to link clinical and health as well as research data to computational models relevant for personalised medicine. As all requirements should be equally understood and fulfilled by users it is important to define them uniformly in an international context. To achieve this the conclusion of our work shall be also discussed in international standardization and technical committees, especially in the case of standards that are still being drawn up, and new standardization projects shall be initiated where necessary. We present a White Paper featuring recommendations for standardization of data integration as well as recommendations for standardization of model validation within a collaborative research context, such that health-related data can be optimally used for translational research and personalised medicine across Europe. As such the White Paper showcases the approach that takes big data in health through harmonized data integration to the most relevant predictive computational models for personalised medicine. As they are refined and validated these models can provide guidance not just how to use data, but also how to best cope with disease and preserve wellbeing in the daily lives of patients.

Authors: Kirstine Belling, Marina Caldara, Catherine Bjerre Collin, Tom Gebhardt, Martin Golebiewski, Tugce Karaderi, Faiz M. Khan, Marc Kirschner, Sylvia Krobitsch, Lars Küpfer, Heike Moser, Flora Musuamba Tschinanu, Mariam Nassar, Tito Poli, Philip Rosenstiel, Dagmar Waltemath, Olaf Wolkehnauer, EU-STANDS4PM consortium

Date Published: 7th Feb 2022

Publication Type: Misc

Computational Models for Clinical Applications in Personalized Medicine-Guidelines and Recommendations for Data Integration and Model Validation.

Abstract (Expand)

The future development of personalized medicine depends on a vast exchange of data from different sources, as well as harmonized integrative analysis of large-scale clinical health and sample data. … Computational-modelling approaches play a key role in the analysis of the underlying molecular processes and pathways that characterize human biology, but they also lead to a more profound understanding of the mechanisms and factors that drive diseases; hence, they allow personalized treatment strategies that are guided by central clinical questions. However, despite the growing popularity of computational-modelling approaches in different stakeholder communities, there are still many hurdles to overcome for their clinical routine implementation in the future. Especially the integration of heterogeneous data from multiple sources and types are challenging tasks that require clear guidelines that also have to comply with high ethical and legal standards. Here, we discuss the most relevant computational models for personalized medicine in detail that can be considered as best-practice guidelines for application in clinical care. We define specific challenges and provide applicable guidelines and recommendations for study design, data acquisition, and operation as well as for model validation and clinical translation and other research areas.

Authors: C. B. Collin, T. Gebhardt, M. Golebiewski, T. Karaderi, M. Hillemanns, F. M. Khan, A. Salehzadeh-Yazdi, M. Kirschner, S. Krobitsch, C. o. n. s. o. r. t. i. u. m. Eu-Stands Pm, L. Kuepfer

Date Published: 26th Jan 2022

Publication Type: Journal

Systems Biology in ELIXIR: modelling in the spotlight

Abstract (Expand)

In this white paper, we describe the founding of a new ELIXIR Community - the Systems Biology Community - and its proposed future contributions to both ELIXIR and the broader community of systems … biologists in Europe and worldwide. The Community believes that the infrastructure aspects of systems biology - databases, (modelling) tools and standards development, as well as training and access to cloud infrastructure - are not only appropriate components of the ELIXIR infrastructure, but will prove key components of ELIXIR’s future support of advanced biological applications and personalised medicine. By way of a series of meetings, the Community identified seven key areas for its future activities, reflecting both future needs and previous and current activities within ELIXIR Platforms and Communities. These are: overcoming barriers to the wider uptake of systems biology; linking new and existing data to systems biology models; interoperability of systems biology resources; further development and embedding of systems medicine; provisioning of modelling as a service; building and coordinating capacity building and training resources; and supporting industrial embedding of systems biology. A set of objectives for the Community has been identified under four main headline areas: Standardisation and Interoperability, Technology, Capacity Building and Training, and Industrial Embedding. These are grouped into short-term (3-year), mid-term (6-year) and long-term (10-year) objectives.

Authors: Vitor Martins dos Santos, Mihail Anton, Barbara Szomolay, Marek Ostaszewski, Ilja Arts, Rui Benfeitas, Victoria Dominguez Del Angel, Polonca Ferk, Dirk Fey, Carole Goble, Martin Golebiewski, Kristina Gruden, Katharina F. Heil, Henning Hermjakob, Pascal Kahlem, Maria I. Klapa, Jasper Koehorst, Alexey Kolodkin, Martina Kutmon, Brane Leskošek, Sébastien Moretti, Wolfgang Müller, Marco Pagni, Tadeja Rezen, Miguel Rocha, Damjana Rozman, David Šafránek, Rahuman S. Malik Sheriff, Maria Suarez Diez, Kristel Van Steen, Hans V Westerhoff, Ulrike Wittig, Katherine Wolstencroft, Anze Zupanic, Chris T. Evelo, John M. Hancock

Date Published: 2022

Publication Type: Journal

EnzymeML-a data exchange format for biocatalysis and enzymology.

Abstract (Expand)

EnzymeML is an XML-based data exchange format that supports the comprehensive documentation of enzymatic data by describing reaction conditions, time courses of substrate and product concentrations, … the kinetic model, and the estimated kinetic constants. EnzymeML is based on the Systems Biology Markup Language, which was extended by implementing the STRENDA Guidelines. An EnzymeML document serves as a container to transfer data between experimental platforms, modeling tools, and databases. EnzymeML supports the scientific community by introducing a standardized data exchange format to make enzymatic data findable, accessible, interoperable, and reusable according to the FAIR data principles. An application programming interface in Python supports the integration of software tools for data acquisition, data analysis, and publication. The feasibility of a seamless data flow using EnzymeML is demonstrated by creating an EnzymeML document from a structured spreadsheet or from a STRENDA DB database entry, by kinetic modeling using the modeling platform COPASI, and by uploading to the enzymatic reaction kinetics database SABIO-RK.

Authors: J. Range, C. Halupczok, J. Lohmann, N. Swainston, C. Kettner, F. T. Bergmann, A. Weidemann, U. Wittig, S. Schnell, J. Pleiss

Date Published: 11th Dec 2021

Publication Type: Journal

Use of a quantitative data report in a hypothetical decision scenario for health policymaking: a computer-assisted laboratory study

Abstract (Expand)

Background: Quantitative data reports are widely produced to inform health policy decisions. Policymakers are expected to critically assess provided information in order to incorporate the best available … evidence into the decision-making process. Many other factors are known to influence this process, but little is known about how quantitative data reports are actually read. We explored the reading behavior of (future) health policy decision-makers, using innovative methods. Methods: We conducted a computer-assisted laboratory study, involving starting and advanced students in medicine and health sciences, and professionals as participants. They read a quantitative data report to inform a decision on the use of resources for long-term care in dementia in a hypothetical decision scenario. Data were collected through eye-tracking, questionnaires, and a brief interview. Eye-tracking data were used to generate ‘heatmaps’ and five measures of reading behavior. The questionnaires provided participants’ perceptions of understandability and helpfulness as well as individual characteristics. Interviews documented reasons for attention to specific report sections. The quantitative analysis was largely descriptive, complemented by Pearson correlations. Interviews were analyzed by qualitative content analysis. Results: In total, 46 individuals participated [students (85%), professionals (15%)]. Eye-tracking observations showed that the participants spent equal time and attention for most parts of the presented report, but were less focused when reading the methods section. The qualitative content analysis identified 29 reasons for attention to a report section related to four topics. Eye-tracking measures were largely unrelated to participants’ perceptions of understandability and helpfulness of the report. Conclusions: Eye-tracking data added information on reading behaviors that were not captured by questionnaires or interviews with health decision-makers.

Authors: Pamela Wronski, Michel Wensing, Sucheta Ghosh, Lukas Gärttner, Wolfgang Müller, Jan Koetsenruijter

Date Published: 1st Dec 2021

Publication Type: Journal

Ten Simple Rules for FAIR Sharing of Experimental and Clinical Data with the Modeling Community

Abstract (Expand)

Science continues to become more interdisciplinary and to involve increasingly complex data sets. Many projects in the biomedical and health-related sciences follow or aim to follow the principles of …rinciples of FAIR data sharing, which has been demonstrated to foster collaboration, to lead to better research outcomes, and to help ensure reproducibility of results. Data generated in the course of biomedical and health research present specific challenges for FAIR sharing in the sense that they are heterogeneous and highly sensitive to context and the needs of protection and privacy. Data sharing must respect these features without impeding timely dissemination of results, so that they can contribute to time-critical advances in medical therapy and treatment. Modeling and simulation of biomedical processes have become established tools, and a global community has been developing algorithms, methodologies, and standards for applying biomedical simulation models in clinical research. However, it can be difficult for clinician scientists to follow the specific rules and recommendations for FAIR data sharing within this domain. We seek to clarify the standard workflow for sharing experimental and clinical data with the simulation modeling community. By following these recommendations, data sharing will be improved, collaborations will become more effective, and the FAIR publication and subsequent reuse of data will become possible at the level of quality necessary to support biomedical and health-related sciences.

Authors: Matthias König, Jan Grzegorzewski, Martin Golebiewski, Henning Hermjakob, Mike Hucka, Brett Olivier, Sarah Keating, David Nickerson, Falk Schreiber, Rahuman Sheriff, Dagmar Waltemath

Date Published: 19th Nov 2021

Publication Type: Journal

Powered by
 (v.1.14.2)
About HITS Publication SEEK | About HITS-Publication-SEEK | Funding and Programmes | Credits
Copyright © 2008 - 2023 The University of Manchester and HITS gGmbH