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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Predicting Gene Expression Noise from Gene Expression Variations
|
|---|---|
| Chapter number | 13 |
| Book title |
Transcriptome Data Analysis
|
| Published in |
Methods in molecular biology, January 2018
|
| DOI | 10.1007/978-1-4939-7710-9_13 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-7709-3, 978-1-4939-7710-9
|
| Authors |
Xiaojian Shao, Ming-an Sun, Shao, Xiaojian, Sun, Ming-an |
| Abstract |
The level of gene expression is known to vary from cell to cell and even in the same cell over time. This variability provides cells with the ability to mitigate environmental stresses and genetic perturbations, and facilitates gene expression evolution. Recently, many valuable gene expression noise data measured at the single-cell level and gene expression variation measured for cell populations have become available. In this chapter, we show how to perform integrative analysis using these data. Specifically, we introduce how to apply a machine learning technique (support vector regression) to explore the relationship between gene expression variations and stochastic noise. |
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 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 10 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 30% |
| Student > Bachelor | 2 | 20% |
| Unspecified | 1 | 10% |
| Researcher | 1 | 10% |
| Student > Master | 1 | 10% |
| Other | 0 | 0% |
| Unknown | 2 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 3 | 30% |
| Agricultural and Biological Sciences | 2 | 20% |
| Computer Science | 1 | 10% |
| Unspecified | 1 | 10% |
| Unknown | 3 | 30% |
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 February 2019.
All research outputs
#15,494,712
of 23,026,672 outputs
Outputs from Methods in molecular biology
#5,390
of 13,170 outputs
Outputs of similar age
#269,811
of 442,370 outputs
Outputs of similar age from Methods in molecular biology
#596
of 1,499 outputs
Altmetric has tracked 23,026,672 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,170 research outputs from this source. They receive a mean Attention Score of 3.4. This one is in the 44th percentile – i.e., 44% 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 442,370 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,499 others from the same source and published within six weeks on either side of this one. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.