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
Species specific approach to the development of web-based antimicrobial peptides prediction tool for cattle
Antimicrobial peptides (AMPs) are the defence molecules of the host gaining extensive attention worldwide as these are natural alternative to chemical antibiotics. Machine learning techniques have capabilities to analyse large biological data for detection of hidden pattern in understanding complex underlying biological problems. Presently, development of resistance to chemical antibiotics in cattle is unsolved and growing problem which needs immediate attention. In the present study, attempt was made to apply machine learning algorithms such as Artificial Neuron Network (ANN) and Support Vector Machine (SVM). It was found that performance of SVM based models for in silico prediction/identification of AMPs of cattle is superior than ANN. A total of 99 AMPs related to cattle collected from various databases and published literature were taken for this study. N-terminus residues, C-terminus residues and full sequences were used for model development and identification/prediction. It was found that best SVM models in this case for C-terminus residues, N-terminus residues and full sequence were with kernels Radial Basis Function (RBF), Sigmoid and RBF with accuracy as 95%, 99% and 97%, respectively. These SVM models were implemented on web server and made available to users at http://cabin.iasri.res.in/amp/ for classification/prediction of novel AMPs of cattle. This computational server can accelerate novel AMP discovery from whole genome proteins of a given cattle species for bulk discovery with very high accuracy. This is the first successful attempt for development of species specific approach for prediction/classification of AMPs, which may be used further as a model in other species as well. (C) 2014 Elsevier B.V. All rights reserved.
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