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X Demographics
Mendeley readers
Attention Score in Context
| Title |
LocText: relation extraction of protein localizations to assist database curation
|
|---|---|
| Published in |
BMC Bioinformatics, January 2018
|
| DOI | 10.1186/s12859-018-2021-9 |
| Pubmed ID | |
| Authors |
Juan Miguel Cejuela, Shrikant Vinchurkar, Tatyana Goldberg, Madhukar Sollepura Prabhu Shankar, Ashish Baghudana, Aleksandar Bojchevski, Carsten Uhlig, André Ofner, Pandu Raharja-Liu, Lars Juhl Jensen, Burkhard Rost |
| Abstract |
The subcellular localization of a protein is an important aspect of its function. However, the experimental annotation of locations is not even complete for well-studied model organisms. Text mining might aid database curators to add experimental annotations from the scientific literature. Existing extraction methods have difficulties to distinguish relationships between proteins and cellular locations co-mentioned in the same sentence. LocText was created as a new method to extract protein locations from abstracts and full texts. LocText learned patterns from syntax parse trees and was trained and evaluated on a newly improved LocTextCorpus. Combined with an automatic named-entity recognizer, LocText achieved high precision (P = 86%±4). After completing development, we mined the latest research publications for three organisms: human (Homo sapiens), budding yeast (Saccharomyces cerevisiae), and thale cress (Arabidopsis thaliana). Examining 60 novel, text-mined annotations, we found that 65% (human), 85% (yeast), and 80% (cress) were correct. Of all validated annotations, 40% were completely novel, i.e. did neither appear in the annotations nor the text descriptions of Swiss-Prot. LocText provides a cost-effective, semi-automated workflow to assist database curators in identifying novel protein localization annotations. The annotations suggested through text-mining would be verified by experts to guarantee high-quality standards of manually-curated databases such as Swiss-Prot. |
X Demographics
The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Germany | 2 | 25% |
| Switzerland | 1 | 13% |
| China | 1 | 13% |
| Poland | 1 | 13% |
| Denmark | 1 | 13% |
| Unknown | 2 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 63% |
| Scientists | 3 | 38% |
Mendeley readers
The data shown below were compiled from readership statistics for 54 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 54 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 9 | 17% |
| Student > Bachelor | 6 | 11% |
| Student > Master | 6 | 11% |
| Student > Ph. D. Student | 6 | 11% |
| Other | 3 | 6% |
| Other | 7 | 13% |
| Unknown | 17 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 11 | 20% |
| Agricultural and Biological Sciences | 9 | 17% |
| Engineering | 5 | 9% |
| Biochemistry, Genetics and Molecular Biology | 3 | 6% |
| Business, Management and Accounting | 2 | 4% |
| Other | 6 | 11% |
| Unknown | 18 | 33% |
Attention Score in Context
This research output has an Altmetric Attention Score of 5. 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 14 October 2019.
All research outputs
#6,162,807
of 23,344,526 outputs
Outputs from BMC Bioinformatics
#2,274
of 7,387 outputs
Outputs of similar age
#123,760
of 443,655 outputs
Outputs of similar age from BMC Bioinformatics
#34
of 119 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 7,387 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 68% 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 443,655 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.
We're also able to compare this research output to 119 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 71% of its contemporaries.