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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Bioinformatics Methods to Deduce Biological Interpretation from Proteomics Data.
|
|---|---|
| Chapter number | 12 |
| Book title |
Proteome Bioinformatics
|
| Published in |
Methods in molecular biology, January 2017
|
| DOI | 10.1007/978-1-4939-6740-7_12 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6738-4, 978-1-4939-6740-7
|
| Authors |
Krishna Patel, Manika Singh, Harsha Gowda |
| Editors |
Shivakumar Keerthikumar, Suresh Mathivanan |
| Abstract |
High-throughput proteomics studies generate large amounts of data. Biological interpretation of these large scale datasets is often challenging. Over the years, several computational tools have been developed to facilitate meaningful interpretation of large-scale proteomics data. In this chapter, we describe various analyses that can be performed and bioinformatics tools and resources that enable users to do the analyses. Many Web-based and stand-alone tools are relatively user-friendly and can be used by most biologists without significant assistance. |
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 % |
|---|---|---|
| Spain | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 26 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Other | 3 | 12% |
| Student > Ph. D. Student | 3 | 12% |
| Professor > Associate Professor | 2 | 8% |
| Professor | 2 | 8% |
| Student > Master | 2 | 8% |
| Other | 2 | 8% |
| Unknown | 12 | 46% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 4 | 15% |
| Agricultural and Biological Sciences | 3 | 12% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 8% |
| Chemical Engineering | 1 | 4% |
| Computer Science | 1 | 4% |
| Other | 1 | 4% |
| Unknown | 14 | 54% |
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 27 February 2018.
All research outputs
#18,493,111
of 22,914,829 outputs
Outputs from Methods in molecular biology
#7,927
of 13,131 outputs
Outputs of similar age
#310,545
of 420,479 outputs
Outputs of similar age from Methods in molecular biology
#692
of 1,074 outputs
Altmetric has tracked 22,914,829 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 13,131 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.
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 420,479 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1,074 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.