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

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

Specifications of standards in systems and synthetic biology: status and developments in 2020
Scientific Databases and Visualisation

Abstract (Expand)

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. …Not specified

Authors: Falk Schreiber, Björn Sommer, Tobias Czauderna, Martin Golebiewski, Thomas E. Gorochowski, Michael Hucka, Sarah M. Keating, Matthias König, Chris Myers, David Nickerson, Dagmar Waltemath

Date Published: 29th Jun 2020

Publication Type: Journal

Towards standardization guidelines for in silico approaches in personalized medicine
Scientific Databases and Visualisation

Abstract (Expand)

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.

Authors: Søren Brunak, Catherine Bjerre Collin, Katharina Eva Ó Cathaoir, Martin Golebiewski, Marc Kirschner, Ingrid Kockum, Heike Moser, Dagmar Waltemath

Date Published: 24th Jul 2020

Publication Type: Journal

Challenges for an enzymatic reaction kinetics database.
Scientific Databases and Visualisation

Abstract (Expand)

The scientific literature contains a tremendous amount of kinetic data describing the dynamic behaviour of biochemical reactions over time. These data are needed for computational modelling to create … models of biochemical reaction networks and to obtain a better understanding of the processes in living cells. To extract the knowledge from the literature, biocurators are required to understand a paper and interpret the data. For modellers, as well as experimentalists, this process is very time consuming because the information is distributed across the publication and, in most cases, is insufficiently structured and often described without standard terminology. In recent years, biological databases for different data types have been developed. The advantages of these databases lie in their unified structure, searchability and the potential for augmented analysis by software, which supports the modelling process. We have developed the SABIO-RK database for biochemical reaction kinetics. In the present review, we describe the challenges for database developers and curators, beginning with an analysis of relevant publications up to the export of database information in a standardized format. The aim of the present review is to draw the experimentalist's attention to the problem (from a data integration point of view) of incompletely and imprecisely written publications. We describe how to lower the barrier to curators and improve this situation. At the same time, we are aware that curating experimental data takes time. There is a community concerned with making the task of publishing data with the proper structure and annotation to ontologies much easier. In this respect, we highlight some useful initiatives and tools.

Authors: U. Wittig, M. Rey, R. Kania, M. Bittkowski, L. Shi, M. Golebiewski, A. Weidemann, W. Muller, I. Rojas

Date Published: 30th Oct 2013

Publication Type: Journal

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