| Title |
NETTAB 2014: From high-throughput structural bioinformatics to integrative systems biology
|
|---|---|
| Published in |
BMC Bioinformatics, March 2016
|
| DOI | 10.1186/s12859-016-0907-y |
| Pubmed ID | |
| Authors |
Paolo Romano, Francesca Cordero |
| Abstract |
The fourteenth NETTAB workshop, NETTAB 2014, was devoted to a range of disciplines going from structural bioinformatics, to proteomics and to integrative systems biology. The topics of the workshop were centred around bioinformatics methods, tools, applications, and perspectives for models, standards and management of high-throughput biological data, structural bioinformatics, functional proteomics, mass spectrometry, drug discovery, and systems biology.43 scientific contributions were presented at NETTAB 2014, including keynote, special guest and tutorial talks, oral communications, and posters. Full papers from some of the best contributions presented at the workshop were later submitted to a special Call for this Supplement.Here, we provide an overview of the workshop and introduce manuscripts that have been accepted for publication in this Supplement. |
X Demographics
The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 9% |
| Italy | 1 | 9% |
| Unknown | 9 | 82% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 55% |
| Scientists | 5 | 45% |
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 | 3 | 27% |
| Researcher | 2 | 18% |
| Student > Doctoral Student | 1 | 9% |
| Other | 1 | 9% |
| Student > Master | 1 | 9% |
| Other | 1 | 9% |
| Unknown | 2 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 27% |
| Agricultural and Biological Sciences | 2 | 18% |
| Biochemistry, Genetics and Molecular Biology | 1 | 9% |
| Social Sciences | 1 | 9% |
| Medicine and Dentistry | 1 | 9% |
| Other | 1 | 9% |
| Unknown | 2 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 20 March 2016.
All research outputs
#5,462,121
of 22,852,911 outputs
Outputs from BMC Bioinformatics
#1,952
of 7,292 outputs
Outputs of similar age
#75,981
of 298,624 outputs
Outputs of similar age from BMC Bioinformatics
#39
of 129 outputs
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So far Altmetric has tracked 7,292 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has gotten more attention than average, scoring higher than 72% of its peers.
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We're also able to compare this research output to 129 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.