51 research outputs found
GraphML-SBGN bidirectional converter for metabolic networks.
No full textpeer reviewedSystems biology researchers need feasible solutions for editing and visualisation of large biological diagrams. Here, we present the ySBGN bidirectional converter that translates metabolic pathways, developed in the general-purpose yEd Graph Editor (using the GraphML format) into the Systems Biology Graphical Notation Markup Language (SBGN-ML) standard format and vice versa. We illustrate the functionality of this converter by applying it to the translation of the ReconMap resource (available in the SBGN-ML format) to the yEd-specific GraphML and back. The ySBGN tool makes possible to draw extensive metabolic diagrams in a powerful general-purpose graph editor while providing results in the standard SBGN format
Analiza comparativă a metodelor şi instrumentelor de preluare a mărimilor antropometrice ale piciorului
No full textTo realize adequate footwear, these products must be based on a last, which characterizes accurately the morphology of the foot. This requirement is possible by using, in the process of the lasts’ designing, of the values of the anthropometric parameters of the foot. To perform the anthropometric measurements, in time, a variety of methods and instruments has been used, which evolved, being sustained by the technical-scientifical progress
StonPy: a tool to parse and query collections of SBGN maps in a graph database.
No full textpeer reviewed[en] SUMMARY: The systems biology graphical notation (SBGN) has become the de facto standard for the graphical representation of molecular maps. Having rapid and easy access to the content of large collections of maps is necessary to perform semantic or graph-based analysis of these resources. To this end, we propose StonPy, a new tool to store and query SBGN maps in a Neo4j graph database. StonPy notably includes a data model that takes into account all three SBGN languages and a completion module to automatically build valid SBGN maps from query results. StonPy is built as a library that can be integrated into other software and offers a command-line interface that allows users to easily perform all operations.
AVAILABILITY AND IMPLEMENTATION: StonPy is implemented in Python 3 under a GPLv3 license. Its code and complete documentation are freely available from https://github.com/adrienrougny/stonpy.
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online
Experiences From FAIRifying Community Data and FAIR Infrastructure in Biomedical Research Domains
No full textFAIR data is considered good data. However, it can be difficult to quantify data FAIRness objectively, without appropriate tooling. To address this issue, FAIR metrics were developed in the early days of the FAIR era. However, to be truly informative, these metrics must be carefully interpreted in the context of a specific domain, and sometimes even of a project. Here, we share our experience with FAIR assessments and FAIRification processes in the biomedical domain. We aim to raise the awareness that “being FAIR” is not an easy goal, neither the principles are easily implemented. FAIR goes far beyond technical implementations: it requires time, expertise, communication and a shift in mindset. 
Experiences From FAIRifying Community Data and FAIR Infrastructure in Biomedical Research Domains
No full textpeer reviewedFAIR data is considered good data. However, it can be difficult to quantify data FAIRness objectively, without appropriate tooling. To address this issue, FAIR metrics were developed in the early days of the FAIR era. However, to be truly informative, these metrics must be carefully interpreted in the context of a specific domain, and sometimes even of a project. Here, we share our experience with FAIR assessments and FAIRification processes in the biomedical domain. We aim to raise the awareness that “being FAIR” is not an easy goal, neither the principles are easily implemented. FAIR goes far beyond technical implementations: it requires time, expertise, communication and a shift in mindset.
STON.pdf
No full textSTON,
SBGN to Neo4j: using graph database technologies for storing
disease-relevant biological pathways and networks
Vasundra
Touré1,
Alexander Mazein2,
Dagmar Waltemath1,
Irina Balaur2,
Ron Henkel1,
Mansoor Saqi2,
Johann Pellet2
and Charles Auffray2
1Department
of Systems Biology and Bioinformatics, University of Rostock, 18051
Rostock, Germany.
2European
Institute for Systems Biology and Medicine (EISBM), Centre National
de la Recherche Scientifique (CNRS), Campus Charles Mérieux -
Université de Lyon - 50 Avenue Tony Garnier, 69007 Lyon, France;
IMI-eTRIKS consortium.
Abstract
Background:
Graph
databases can be successfully applied in Systems Biology and in
Systems Medicine for managing extensive and complex information.
Ultimately, graphs are a natural way of representing biological
networks. The use of graph databases enables efficient storing and
processing of biological relationships, and it can lead to a better
response time when querying the data.
Objectives:
We
would like to use graph databases structure to store and explore
biological pathways and networks.
Method:
Translation rules have been determined to represent biological
reaction networks in a graph model, that is to say as nodes,
relationships and properties. The reaction networks are provided in
the graphical standard Systems Biology Graphical Notation (SBGN). The
graph model is stored in a Neo4j database.
Results:
We
present the Java-based framework STON (SBGN TO Neo4j) to import and
translate metabolic, signalling and gene regulatory pathways. On the
poster, we show examples of networks representing parts of the Asthma
Map, the iNOS pathway (a SBGN use case network).
Conclusion:
STON
exploits the power of a graph database for the search in complex
biological pathways.
Importing biological pathways in a graph database enables:
1)
identification of functional sub-modules and comparing different
networks in order to discover common patterns.
2)
merging multiple diagrams for creating large comprehensive networks
for empowering systems medicine approaches.
Availability:
The STON framework is available here:
http://sourceforge.net/projects/ston/.
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CALIFRAME: a proposed method of calibrating reporting guidelines with FAIR principles to foster reproducibility of AI research in medicine
No full textBackground
Procedural and reporting guidelines are crucial in framing scientific practices and communications among researchers and the broader community. These guidelines aim to ensure transparency, reproducibility, and reliability in scientific research. Despite several methodological frameworks proposed by various initiatives to foster reproducibility, challenges such as data leakage and reproducibility remain prevalent. Recent studies have highlighted the transformative potential of incorporating the FAIR (Findable, Accessible, Interoperable, and Reusable) principles into workflows, particularly in contexts like software and machine learning model development, to promote open science.
Objective
This study aims to introduce a comprehensive framework, designed to calibrate existing reporting guidelines against the FAIR principles. The goal is to enhance reproducibility and promote open science by integrating these principles into the scientific reporting process.
Methods
We employed the “Best fit” framework synthesis approach which involves systematically reviewing and synthesizing existing frameworks and guidelines to identify best practices and gaps. We then proposed a series of defined workflows to align reporting guidelines with FAIR principles. A use case was developed to demonstrate the practical application of the framework.
Results
The integration of FAIR principles with established reporting guidelines through the framework effectively bridges the gap between FAIR metrics and traditional reporting standards. The framework provides a structured approach to enhance the findability, accessibility, interoperability, and reusability of scientific data and outputs. The use case demonstrated the practical benefits of the framework, showing improved data management and reporting practices.
Discussion
The framework addresses critical challenges in scientific research, such as data leakage and reproducibility issues. By embedding FAIR principles into reporting guidelines, the framework ensures that scientific outputs are more transparent, reliable, and reusable. This integration not only benefits researchers by improving data management practices but also enhances the overall scientific process by promoting open science and collaboration.
Conclusion
The proposed framework successfully combines FAIR principles with reporting guidelines, offering a robust solution to enhance reproducibility and open science. This framework can be applied across various contexts, including software and machine learning model development stages, to foster a more transparent and collaborative scientific environment
A Drug Repurposing Pipeline Based on Bladder Cancer Integrated Proteotranscriptomics Signatures
No full textDelivering better care for patients with bladder cancer (BC) necessitates the development of novel therapeutic strategies that address both the high disease heterogeneity and the limitations of the current therapeutic modalities, such as drug low efficacy and patient resistance acquisition. Drug repurposing is a cost-effective strategy that targets the reuse of existing drugs for new therapeutic purposes. Such a strategy could open new avenues toward more effective BC treatment. BC patients' multi-omics signatures can be used to guide the investigation of existing drugs that show an effective therapeutic potential through drug repurposing. In this book chapter, we present an integrated multilayer approach that includes cross-omics analyses from publicly available transcriptomics and proteomics data derived from BC tissues and cell lines that were investigated for the development of disease-specific signatures. These signatures are subsequently used as input for a signature-based repurposing approach using the Connectivity Map (CMap) tool. We further explain the steps that may be followed to identify and select existing drugs of increased potential for repurposing in BC patients.ReDiREC
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