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Attention Score in Context
| Title |
Sparse representation approaches for the classification of high-dimensional biological data
|
|---|---|
| Published in |
BMC Systems Biology, October 2013
|
| DOI | 10.1186/1752-0509-7-s4-s6 |
| Pubmed ID | |
| Authors |
Yifeng Li, Alioune Ngom |
| Abstract |
High-throughput genomic and proteomic data have important applications in medicine including prevention, diagnosis, treatment, and prognosis of diseases, and molecular biology, for example pathway identification. Many of such applications can be formulated to classification and dimension reduction problems in machine learning. There are computationally challenging issues with regards to accurately classifying such data, and which due to dimensionality, noise and redundancy, to name a few. The principle of sparse representation has been applied to analyzing high-dimensional biological data within the frameworks of clustering, classification, and dimension reduction approaches. However, the existing sparse representation methods are inefficient. The kernel extensions are not well addressed either. Moreover, the sparse representation techniques have not been comprehensively studied yet in bioinformatics. |
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 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Taiwan | 1 | 5% |
| Unknown | 19 | 95% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 5 | 25% |
| Researcher | 4 | 20% |
| Student > Doctoral Student | 2 | 10% |
| Student > Ph. D. Student | 2 | 10% |
| Student > Bachelor | 1 | 5% |
| Other | 2 | 10% |
| Unknown | 4 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 15% |
| Agricultural and Biological Sciences | 3 | 15% |
| Biochemistry, Genetics and Molecular Biology | 2 | 10% |
| Medicine and Dentistry | 2 | 10% |
| Engineering | 2 | 10% |
| Other | 4 | 20% |
| Unknown | 4 | 20% |
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 08 November 2014.
All research outputs
#17,285,036
of 25,373,627 outputs
Outputs from BMC Systems Biology
#651
of 1,132 outputs
Outputs of similar age
#140,832
of 224,682 outputs
Outputs of similar age from BMC Systems Biology
#20
of 35 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 21st percentile – i.e., 21% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,132 research outputs from this source. They receive a mean Attention Score of 3.7. This one is in the 31st percentile – i.e., 31% 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 224,682 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 35 others from the same source and published within six weeks on either side of this one. This one is in the 22nd percentile – i.e., 22% of its contemporaries scored the same or lower than it.