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Mendeley readers
Attention Score in Context
| Title |
Active module identification in intracellular networks using a memetic algorithm with a new binary decoding scheme
|
|---|---|
| Published in |
BMC Genomics, March 2017
|
| DOI | 10.1186/s12864-017-3495-y |
| Pubmed ID | |
| Authors |
Dong Li, Zhisong Pan, Guyu Hu, Zexuan Zhu, Shan He |
| Abstract |
Active modules are connected regions in biological network which show significant changes in expression over particular conditions. The identification of such modules is important since it may reveal the regulatory and signaling mechanisms that associate with a given cellular response. In this paper, we propose a novel active module identification algorithm based on a memetic algorithm. We propose a novel encoding/decoding scheme to ensure the connectedness of the identified active modules. Based on the scheme, we also design and incorporate a local search operator into the memetic algorithm to improve its performance. The effectiveness of proposed algorithm is validated on both small and large protein interaction networks. |
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 States | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 16 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 31% |
| Other | 2 | 13% |
| Student > Bachelor | 2 | 13% |
| Student > Ph. D. Student | 2 | 13% |
| Student > Master | 2 | 13% |
| Other | 2 | 13% |
| Unknown | 1 | 6% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 50% |
| Agricultural and Biological Sciences | 2 | 13% |
| Biochemistry, Genetics and Molecular Biology | 2 | 13% |
| Mathematics | 1 | 6% |
| Unknown | 3 | 19% |
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 September 2017.
All research outputs
#17,884,576
of 22,961,203 outputs
Outputs from BMC Genomics
#7,603
of 10,686 outputs
Outputs of similar age
#221,127
of 307,967 outputs
Outputs of similar age from BMC Genomics
#133
of 200 outputs
Altmetric has tracked 22,961,203 research outputs across all sources so far. This one is in the 19th percentile – i.e., 19% of other outputs scored the same or lower than it.
So far Altmetric has tracked 10,686 research outputs from this source. They receive a mean Attention Score of 4.7. This one is in the 23rd percentile – i.e., 23% 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 307,967 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 23rd percentile – i.e., 23% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 200 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.