Genetic Epidemiology
Springer New York
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Attention Score in Context
| Chapter title |
Methods of Analysis and Meta-Analysis for Identifying Differentially Expressed Genes
|
|---|---|
| Book title |
Genetic Epidemiology
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7868-7_12 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7867-0, 978-1-4939-7868-7
|
| Authors |
Panagiota I Kontou, Athanasia Pavlopoulou, Pantelis G. Bagos |
| Abstract |
Microarray approaches are widely used high-throughput techniques to assess simultaneously the expression of thousands of genes under certain conditions and study the effects of certain treatments, diseases, and developmental stages. The traditional way to perform such experiments is to design oligonucleotide hybridization probes that correspond to specific genes and then measure the expression of the genes in order to determine which of them are up- or down-regulated compared to a condition that is used as a control. Hitherto, individual experiments cannot capture the bigger picture of how a biological system works and, therefore, data integration from multiple experimental studies and external data repositories is necessary to understand the function of genes and their expression patterns under certain conditions. Therefore, the development of methods for handling, integrating, comparing, interpreting and visualizing microarray data is necessary. The selection of an appropriate method for analysing microarray datasets is not an easy task. In this chapter, we provide an overview of the various methods developed for microarray data analysis, as well as suggestions for choosing the appropriate method for microarray meta-analysis. |
X Demographics
The data shown below were collected from the profiles of 2 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 Kingdom | 1 | 50% |
| Colombia | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Scientists | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 14 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 29% |
| Professor | 1 | 7% |
| Student > Master | 1 | 7% |
| Unknown | 8 | 57% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 4 | 29% |
| Agricultural and Biological Sciences | 1 | 7% |
| Medicine and Dentistry | 1 | 7% |
| Engineering | 1 | 7% |
| Unknown | 7 | 50% |
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 12 June 2018.
All research outputs
#18,639,173
of 23,090,520 outputs
Outputs from Methods in molecular biology
#7,987
of 13,206 outputs
Outputs of similar age
#330,849
of 442,629 outputs
Outputs of similar age from Methods in molecular biology
#950
of 1,499 outputs
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So far Altmetric has tracked 13,206 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 24th percentile – i.e., 24% of its peers scored the same or lower than it.
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