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Mendeley readers
Attention Score in Context
| Chapter title |
Aggregation Effect in Microarray Data Analysis
|
|---|---|
| Chapter number | 11 |
| Book title |
Statistical Methods for Microarray Data Analysis
|
| Published in |
Methods in molecular biology, January 2013
|
| DOI | 10.1007/978-1-60327-337-4_11 |
| Pubmed ID | |
| Book ISBNs |
978-1-60327-336-7, 978-1-60327-337-4
|
| Authors |
Linlin Chen, Anthony Almudevar, Lev Klebanov |
| Editors |
Andrei Y. Yakovlev, Lev Klebanov, Daniel Gaile |
| Abstract |
Inferring gene regulatory networks from microarray data has become a popular activity in recent years, resulting in an ever-increasing volume of publications. There are many pitfalls in network analysis that remain either unnoticed or scantily understood. A critical discussion of such pitfalls is long overdue. Here we discuss one feature of microarray data the investigators need to be aware of when embarking on a study of putative associations between elements of networks and pathways. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 4 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 4 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Professor > Associate Professor | 2 | 50% |
| Professor | 1 | 25% |
| Other | 1 | 25% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 2 | 50% |
| Biochemistry, Genetics and Molecular Biology | 1 | 25% |
| Mathematics | 1 | 25% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 19 February 2013.
All research outputs
#18,329,207
of 22,696,971 outputs
Outputs from Methods in molecular biology
#7,835
of 13,046 outputs
Outputs of similar age
#218,045
of 280,828 outputs
Outputs of similar age from Methods in molecular biology
#222
of 342 outputs
Altmetric has tracked 22,696,971 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,046 research outputs from this source. They receive a mean Attention Score of 3.3. This one is in the 24th percentile – i.e., 24% 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 280,828 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 342 others from the same source and published within six weeks on either side of this one. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.