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X Demographics
Mendeley readers
Attention Score in Context
| Title |
QTLTableMiner++: semantic mining of QTL tables in scientific articles
|
|---|---|
| Published in |
BMC Bioinformatics, May 2018
|
| DOI | 10.1186/s12859-018-2165-7 |
| Pubmed ID | |
| Authors |
Gurnoor Singh, Arnold Kuzniar, Erik M. van Mulligen, Anand Gavai, Christian W. Bachem, Richard G.F. Visser, Richard Finkers |
| Abstract |
A quantitative trait locus (QTL) is a genomic region that correlates with a phenotype. Most of the experimental information about QTL mapping studies is described in tables of scientific publications. Traditional text mining techniques aim to extract information from unstructured text rather than from tables. We present QTLTableMiner++ (QTM), a table mining tool that extracts and semantically annotates QTL information buried in (heterogeneous) tables of plant science literature. QTM is a command line tool written in the Java programming language. This tool takes scientific articles from the Europe PMC repository as input, extracts QTL tables using keyword matching and ontology-based concept identification. The tables are further normalized using rules derived from table properties such as captions, column headers and table footers. Furthermore, table columns are classified into three categories namely column descriptors, properties and values based on column headers and data types of cell entries. Abbreviations found in the tables are expanded using the Schwartz and Hearst algorithm. Finally, the content of QTL tables is semantically enriched with domain-specific ontologies (e.g. Crop Ontology, Plant Ontology and Trait Ontology) using the Apache Solr search platform and the results are stored in a relational database and a text file. The performance of the QTM tool was assessed by precision and recall based on the information retrieved from two manually annotated corpora of open access articles, i.e. QTL mapping studies in tomato (Solanum lycopersicum) and in potato (S. tuberosum). In summary, QTM detected QTL statements in tomato with 74.53% precision and 92.56% recall and in potato with 82.82% precision and 98.94% recall. QTM is a unique tool that aids in providing QTL information in machine-readable and semantically interoperable formats. |
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 States | 3 | 27% |
| United Kingdom | 2 | 18% |
| France | 2 | 18% |
| Spain | 1 | 9% |
| Unknown | 3 | 27% |
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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 35 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 20% |
| Student > Ph. D. Student | 5 | 14% |
| Student > Master | 4 | 11% |
| Student > Doctoral Student | 3 | 9% |
| Lecturer | 2 | 6% |
| Other | 4 | 11% |
| Unknown | 10 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 31% |
| Computer Science | 6 | 17% |
| Medicine and Dentistry | 3 | 9% |
| Biochemistry, Genetics and Molecular Biology | 1 | 3% |
| Arts and Humanities | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 11 | 31% |
Attention Score in Context
This research output has an Altmetric Attention Score of 9. 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 15 November 2019.
All research outputs
#3,881,433
of 23,344,526 outputs
Outputs from BMC Bioinformatics
#1,469
of 7,387 outputs
Outputs of similar age
#76,118
of 331,527 outputs
Outputs of similar age from BMC Bioinformatics
#26
of 121 outputs
Altmetric has tracked 23,344,526 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
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 done well, scoring higher than 80% 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 331,527 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 77% of its contemporaries.
We're also able to compare this research output to 121 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.