The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
A computational system biology approach to construct gene regulatory networks for salinity response in rice (Oryza sativa)
Salinity is one of the most common abiotic stress which limits agricultural crop production. Salinity stress tolerance in rice (Oryza sativa L.) is an important trait controlled by various genes. The mechanism of salinity stress response in rice is quite complex. Modelling and construction of genetic regulatory networks is an important tool and can be used for understanding this underlying mechanism. This paper considers the problem of modeling and construction of Gene Regulatory Networks using Multiple Linear Regression and Singular Value Decomposition approach coupled with a number of computational tools. The gene networks constructed by using this approach satisfied the scale free property of biological networks and such networks can be used to extract valuable information on the transcription factors, which are salt responsive. The gene ontology enrichment analysis of selected nodes is performed. The developed model can also be used for predicting the gene responses under stress condition and the result shows that the model fits well for the given gene expression data in rice. In this paper, we have identified ten target genes and a series of potential transcription factors for each target gene in rice which are highly salt responsive.
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