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X Demographics
Mendeley readers
Attention Score in Context
| Title |
The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text
|
|---|---|
| Published in |
BMC Bioinformatics, October 2011
|
| DOI | 10.1186/1471-2105-12-s8-s3 |
| Pubmed ID | |
| Authors |
Martin Krallinger, Miguel Vazquez, Florian Leitner, David Salgado, Andrew Chatr-aryamontri, Andrew Winter, Livia Perfetto, Leonardo Briganti, Luana Licata, Marta Iannuccelli, Luisa Castagnoli, Gianni Cesareni, Mike Tyers, Gerold Schneider, Fabio Rinaldi, Robert Leaman, Graciela Gonzalez, Sergio Matos, Sun Kim, W John Wilbur, Luis Rocha, Hagit Shatkay, Ashish V Tendulkar, Shashank Agarwal, Feifan Liu, Xinglong Wang, Rafal Rak, Keith Noto, Charles Elkan, Zhiyong Lu, Rezarta Islamaj Dogan, Jean-Fred Fontaine, Miguel A Andrade-Navarro, Alfonso Valencia |
| Abstract |
Determining usefulness of biomedical text mining systems requires realistic task definition and data selection criteria without artificial constraints, measuring performance aspects that go beyond traditional metrics. The BioCreative III Protein-Protein Interaction (PPI) tasks were motivated by such considerations, trying to address aspects including how the end user would oversee the generated output, for instance by providing ranked results, textual evidence for human interpretation or measuring time savings by using automated systems. Detecting articles describing complex biological events like PPIs was addressed in the Article Classification Task (ACT), where participants were asked to implement tools for detecting PPI-describing abstracts. Therefore the BCIII-ACT corpus was provided, which includes a training, development and test set of over 12,000 PPI relevant and non-relevant PubMed abstracts labeled manually by domain experts and recording also the human classification times. The Interaction Method Task (IMT) went beyond abstracts and required mining for associations between more than 3,500 full text articles and interaction detection method ontology concepts that had been applied to detect the PPIs reported in them. |
X Demographics
The data shown below were collected from the profiles of 4 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 States | 2 | 50% |
| Unknown | 2 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 75% |
| Scientists | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 139 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 4 | 3% |
| United Kingdom | 2 | 1% |
| Spain | 2 | 1% |
| Brazil | 1 | <1% |
| Germany | 1 | <1% |
| Netherlands | 1 | <1% |
| Unknown | 128 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 33 | 24% |
| Student > Ph. D. Student | 28 | 20% |
| Student > Master | 20 | 14% |
| Professor | 8 | 6% |
| Other | 7 | 5% |
| Other | 22 | 16% |
| Unknown | 21 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 52 | 37% |
| Agricultural and Biological Sciences | 22 | 16% |
| Biochemistry, Genetics and Molecular Biology | 10 | 7% |
| Medicine and Dentistry | 5 | 4% |
| Engineering | 4 | 3% |
| Other | 16 | 12% |
| Unknown | 30 | 22% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 04 April 2013.
All research outputs
#12,581,569
of 22,703,044 outputs
Outputs from BMC Bioinformatics
#3,591
of 7,254 outputs
Outputs of similar age
#79,753
of 132,747 outputs
Outputs of similar age from BMC Bioinformatics
#46
of 83 outputs
Altmetric has tracked 22,703,044 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,254 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 50% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 132,747 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 83 others from the same source and published within six weeks on either side of this one. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.