| Title |
Microarray enriched gene rank
|
|---|---|
| Published in |
BioData Mining, January 2015
|
| DOI | 10.1186/s13040-014-0033-1 |
| Pubmed ID | |
| Authors |
Eugene Demidenko |
| Abstract |
We develop a new concept that reflects how genes are connected based on microarray data using the coefficient of determination (the squared Pearson correlation coefficient). Our gene rank combines a priori knowledge about gene connectivity, say, from the Gene Ontology (GO) database, and the microarray expression data at hand, called the microarray enriched gene rank, or simply gene rank (GR). GR, similarly to Google PageRank, is defined in a recursive fashion and is computed as the left maximum eigenvector of a stochastic matrix derived from microarray expression data. An efficient algorithm is devised that allows computation of GR for 50 thousand genes with 500 samples within minutes on a personal computer using the public domain statistical package R. |
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 | 1 | 25% |
| Germany | 1 | 25% |
| 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 31 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 6 | 19% |
| Student > Ph. D. Student | 5 | 16% |
| Student > Master | 4 | 13% |
| Student > Doctoral Student | 2 | 6% |
| Lecturer | 2 | 6% |
| Other | 6 | 19% |
| Unknown | 6 | 19% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 10 | 32% |
| Computer Science | 7 | 23% |
| Biochemistry, Genetics and Molecular Biology | 3 | 10% |
| Medicine and Dentistry | 2 | 6% |
| Mathematics | 1 | 3% |
| Other | 2 | 6% |
| Unknown | 6 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 66. 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 29 July 2015.
All research outputs
#543,840
of 22,778,347 outputs
Outputs from BioData Mining
#4
of 307 outputs
Outputs of similar age
#7,840
of 352,126 outputs
Outputs of similar age from BioData Mining
#1
of 14 outputs
Altmetric has tracked 22,778,347 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 97th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 307 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has done particularly well, scoring higher than 98% of its peers.
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We're also able to compare this research output to 14 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.