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Mendeley readers
Attention Score in Context
| Title |
A method for automatically extracting infectious disease-related primers and probes from the literature
|
|---|---|
| Published in |
BMC Bioinformatics, August 2010
|
| DOI | 10.1186/1471-2105-11-410 |
| Pubmed ID | |
| Authors |
Miguel García-Remesal, Alejandro Cuevas, Victoria López-Alonso, Guillermo López-Campos, Guillermo de la Calle, Diana de la Iglesia, David Pérez-Rey, José Crespo, Fernando Martín-Sánchez, Víctor Maojo |
| Abstract |
Primer and probe sequences are the main components of nucleic acid-based detection systems. Biologists use primers and probes for different tasks, some related to the diagnosis and prescription of infectious diseases. The biological literature is the main information source for empirically validated primer and probe sequences. Therefore, it is becoming increasingly important for researchers to navigate this important information. In this paper, we present a four-phase method for extracting and annotating primer/probe sequences from the literature. These phases are: (1) convert each document into a tree of paper sections, (2) detect the candidate sequences using a set of finite state machine-based recognizers, (3) refine problem sequences using a rule-based expert system, and (4) annotate the extracted sequences with their related organism/gene information. |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Mexico | 2 | 6% |
| China | 1 | 3% |
| Brazil | 1 | 3% |
| Unknown | 28 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 31% |
| Student > Ph. D. Student | 8 | 25% |
| Professor | 4 | 13% |
| Other | 3 | 9% |
| Student > Master | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 3 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 34% |
| Biochemistry, Genetics and Molecular Biology | 6 | 19% |
| Computer Science | 4 | 13% |
| Engineering | 3 | 9% |
| Nursing and Health Professions | 1 | 3% |
| Other | 4 | 13% |
| Unknown | 3 | 9% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. 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 03 March 2021.
All research outputs
#1,880,505
of 22,721,584 outputs
Outputs from BMC Bioinformatics
#466
of 7,261 outputs
Outputs of similar age
#6,736
of 94,450 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 53 outputs
Altmetric has tracked 22,721,584 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,261 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 done particularly well, scoring higher than 93% 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 94,450 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 53 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 92% of its contemporaries.