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X Demographics
Mendeley readers
Attention Score in Context
| Chapter title |
Integrating Proteomics Profiling Data Sets: A Network Perspective
|
|---|---|
| Chapter number | 14 |
| Book title |
Clinical Proteomics
|
| Published in |
Methods in molecular biology, October 2014
|
| DOI | 10.1007/978-1-4939-1872-0_14 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-1871-3, 978-1-4939-1872-0
|
| Authors |
Akshay Bhat, Mohammed Dakna, Harald Mischak, Bhat, Akshay, Dakna, Mohammed, Mischak, Harald |
| Editors |
Antonia Vlahou, Manousos Makridakis |
| Abstract |
Understanding disease mechanisms often requires complex and accurate integration of cellular pathways and molecular networks. Systems biology offers the possibility to provide a comprehensive map of the cell's intricate wiring network, which can ultimately lead to decipher the disease phenotype. Here, we describe what biological pathways are, how they function in normal and abnormal cellular systems, limitations faced by databases for integrating data, and highlight how network models are emerging as a powerful integrative framework to understand and interpret the roles of proteins and peptides in diseases. |
X Demographics
The data shown below were collected from the profiles of 6 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 1 | 17% |
| United Kingdom | 1 | 17% |
| Unknown | 4 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 8 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 1 | 13% |
| Unknown | 7 | 88% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 2 | 25% |
| Other | 1 | 13% |
| Student > Master | 1 | 13% |
| Professor | 1 | 13% |
| Professor > Associate Professor | 1 | 13% |
| Other | 1 | 13% |
| Unknown | 1 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Agricultural and Biological Sciences | 3 | 38% |
| Computer Science | 2 | 25% |
| Biochemistry, Genetics and Molecular Biology | 2 | 25% |
| Unknown | 1 | 13% |
Attention Score in Context
This research output has an Altmetric Attention Score of 3. 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 02 July 2015.
All research outputs
#7,540,801
of 23,006,268 outputs
Outputs from Methods in molecular biology
#2,341
of 13,160 outputs
Outputs of similar age
#85,007
of 259,266 outputs
Outputs of similar age from Methods in molecular biology
#15
of 126 outputs
Altmetric has tracked 23,006,268 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 13,160 research outputs from this source. They receive a mean Attention Score of 3.4. This one has done well, scoring higher than 76% 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 259,266 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 55% of its contemporaries.
We're also able to compare this research output to 126 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.