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| Title |
The bacterial interlocked process ONtology (BiPON): a systemic multi-scale unified representation of biological processes in prokaryotes
|
|---|---|
| Published in |
Journal of Biomedical Semantics, November 2017
|
| DOI | 10.1186/s13326-017-0165-6 |
| Pubmed ID | |
| Authors |
Vincent J. Henry, Anne Goelzer, Arnaud Ferré, Stephan Fischer, Marc Dinh, Valentin Loux, Christine Froidevaux, Vincent Fromion |
| Abstract |
High-throughput technologies produce huge amounts of heterogeneous biological data at all cellular levels. Structuring these data together with biological knowledge is a critical issue in biology and requires integrative tools and methods such as bio-ontologies to extract and share valuable information. In parallel, the development of recent whole-cell models using a systemic cell description opened alternatives for data integration. Integrating a systemic cell description within a bio-ontology would help to progress in whole-cell data integration and modeling synergistically. We present BiPON, an ontology integrating a multi-scale systemic representation of bacterial cellular processes. BiPON consists in of two sub-ontologies, bioBiPON and modelBiPON. bioBiPON organizes the systemic description of biological information while modelBiPON describes the mathematical models (including parameters) associated with biological processes. bioBiPON and modelBiPON are related using bridge rules on classes during automatic reasoning. Biological processes are thus automatically related to mathematical models. 37% of BiPON classes stem from different well-established bio-ontologies, while the others have been manually defined and curated. Currently, BiPON integrates the main processes involved in bacterial gene expression processes. BiPON is a proof of concept of the way to combine formally systems biology and bio-ontology. The knowledge formalization is highly flexible and generic. Most of the known cellular processes, new participants or new mathematical models could be inserted in BiPON. Altogether, BiPON opens up promising perspectives for knowledge integration and sharing and can be used by biologists, systems and computational biologists, and the emerging community of whole-cell modeling. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 11 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 2 | 18% |
| Researcher | 2 | 18% |
| Student > Master | 2 | 18% |
| Lecturer | 1 | 9% |
| Professor > Associate Professor | 1 | 9% |
| Other | 1 | 9% |
| Unknown | 2 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 3 | 27% |
| Biochemistry, Genetics and Molecular Biology | 2 | 18% |
| Environmental Science | 1 | 9% |
| Psychology | 1 | 9% |
| Social Sciences | 1 | 9% |
| Other | 1 | 9% |
| Unknown | 2 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 December 2017.
All research outputs
#18,836,331
of 23,344,526 outputs
Outputs from Journal of Biomedical Semantics
#302
of 367 outputs
Outputs of similar age
#327,881
of 439,996 outputs
Outputs of similar age from Journal of Biomedical Semantics
#7
of 7 outputs
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So far Altmetric has tracked 367 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 8th percentile – i.e., 8% of its peers scored the same or lower than it.
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