You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Pairwise gene GO-based measures for biclustering of high-dimensional expression data
|
|---|---|
| Published in |
BioData Mining, March 2018
|
| DOI | 10.1186/s13040-018-0165-9 |
| Pubmed ID | |
| Authors |
Juan A. Nepomuceno, Alicia Troncoso, Isabel A. Nepomuceno-Chamorro, Jesús S. Aguilar-Ruiz |
| Abstract |
Biclustering algorithms search for groups of genes that share the same behavior under a subset of samples in gene expression data. Nowadays, the biological knowledge available in public repositories can be used to drive these algorithms to find biclusters composed of groups of genes functionally coherent. On the other hand, a distance among genes can be defined according to their information stored in Gene Ontology (GO). Gene pairwise GO semantic similarity measures report a value for each pair of genes which establishes their functional similarity. A scatter search-based algorithm that optimizes a merit function that integrates GO information is studied in this paper. This merit function uses a term that addresses the information through a GO measure. The effect of two possible different gene pairwise GO measures on the performance of the algorithm is analyzed. Firstly, three well known yeast datasets with approximately one thousand of genes are studied. Secondly, a group of human datasets related to clinical data of cancer is also explored by the algorithm. Most of these data are high-dimensional datasets composed of a huge number of genes. The resultant biclusters reveal groups of genes linked by a same functionality when the search procedure is driven by one of the proposed GO measures. Furthermore, a qualitative biological study of a group of biclusters show their relevance from a cancer disease perspective. It can be concluded that the integration of biological information improves the performance of the biclustering process. The two different GO measures studied show an improvement in the results obtained for the yeast dataset. However, if datasets are composed of a huge number of genes, only one of them really improves the algorithm performance. This second case constitutes a clear option to explore interesting datasets from a clinical point of view. |
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 % |
|---|---|---|
| Spain | 3 | 75% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 3 | 75% |
| Members of the public | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 28 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 28 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Librarian | 3 | 11% |
| Student > Bachelor | 3 | 11% |
| Student > Ph. D. Student | 3 | 11% |
| Researcher | 3 | 11% |
| Other | 2 | 7% |
| Other | 7 | 25% |
| Unknown | 7 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 29% |
| Biochemistry, Genetics and Molecular Biology | 3 | 11% |
| Mathematics | 3 | 11% |
| Engineering | 2 | 7% |
| Social Sciences | 2 | 7% |
| Other | 2 | 7% |
| Unknown | 8 | 29% |
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 14 April 2018.
All research outputs
#13,070,764
of 23,031,582 outputs
Outputs from BioData Mining
#174
of 309 outputs
Outputs of similar age
#160,009
of 330,033 outputs
Outputs of similar age from BioData Mining
#2
of 5 outputs
Altmetric has tracked 23,031,582 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 309 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
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 330,033 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 50% of its contemporaries.
We're also able to compare this research output to 5 others from the same source and published within six weeks on either side of this one. This one has scored higher than 3 of them.