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Mendeley readers
Attention Score in Context
| Chapter title |
Combinatorial Optimization Models for Finding Genetic Signatures from Gene Expression Datasets
|
|---|---|
| Chapter number | 19 |
| Book title |
Bioinformatics
|
| Published in |
Methods in molecular biology, January 2008
|
| DOI | 10.1007/978-1-60327-429-6_19 |
| Pubmed ID | |
| Book ISBNs |
978-1-60327-428-9, 978-1-60327-429-6
|
| Authors |
Regina Berretta, Wagner Costa, Pablo Moscato, Berretta, Regina, Costa, Wagner, Moscato, Pablo |
| Abstract |
The aim of this chapter is to present combinatorial optimization models and techniques for the analysis of microarray datasets. The chapter illustrates the application of a novel objective function that guides the search for high-quality solutions for sequential ordering of expression profiles. The approach is unsupervised and a metaheuristic method (a memetic algorithm) is used to provide high-quality solutions. For the problem of selecting discriminative groups of genes, we used a supervised method that has provided good results in a variety of datasets. This chapter illustrates the application of these models in an Alzheimer's disease microarray dataset. |
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 % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 4% |
| Australia | 1 | 4% |
| Unknown | 21 | 91% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 7 | 30% |
| Student > Ph. D. Student | 6 | 26% |
| Student > Master | 3 | 13% |
| Professor > Associate Professor | 2 | 9% |
| Professor | 1 | 4% |
| Other | 3 | 13% |
| Unknown | 1 | 4% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 11 | 48% |
| Computer Science | 4 | 17% |
| Psychology | 2 | 9% |
| Medicine and Dentistry | 2 | 9% |
| Mathematics | 1 | 4% |
| Other | 2 | 9% |
| Unknown | 1 | 4% |
Attention Score in Context
This research output has an Altmetric Attention Score of 34. 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 03 February 2015.
All research outputs
#983,304
of 22,715,151 outputs
Outputs from Methods in molecular biology
#91
of 13,079 outputs
Outputs of similar age
#2,963
of 155,927 outputs
Outputs of similar age from Methods in molecular biology
#4
of 87 outputs
Altmetric has tracked 22,715,151 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,079 research outputs from this source. They receive a mean Attention Score of 3.3. This one has done particularly well, scoring higher than 99% 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 155,927 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 98% of its contemporaries.
We're also able to compare this research output to 87 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 95% of its contemporaries.