| Title |
Prediction of Subcellular Localization of Apoptosis Protein Using Chou’s Pseudo Amino Acid Composition
|
|---|---|
| Published in |
Acta Biotheoretica, January 2009
|
| DOI | 10.1007/s10441-008-9067-4 |
| Pubmed ID | |
| Authors |
Hao Lin, Hao Wang, Hui Ding, Ying-Li Chen, Qian-Zhong Li |
| Abstract |
Apoptosis proteins play an essential role in regulating a balance between cell proliferation and death. The successful prediction of subcellular localization of apoptosis proteins directly from primary sequence is much benefited to understand programmed cell death and drug discovery. In this paper, by use of Chou's pseudo amino acid composition (PseAAC), a total of 317 apoptosis proteins are predicted by support vector machine (SVM). The jackknife cross-validation is applied to test predictive capability of proposed method. The predictive results show that overall prediction accuracy is 91.1% which is higher than previous methods. Furthermore, another dataset containing 98 apoptosis proteins is examined by proposed method. The overall predicted successful rate is 92.9%. |
Mendeley readers
The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 1 | 8% |
| Unknown | 12 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Professor | 3 | 23% |
| Student > Ph. D. Student | 3 | 23% |
| Student > Bachelor | 1 | 8% |
| Student > Master | 1 | 8% |
| Researcher | 1 | 8% |
| Other | 1 | 8% |
| Unknown | 3 | 23% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 3 | 23% |
| Biochemistry, Genetics and Molecular Biology | 2 | 15% |
| Computer Science | 2 | 15% |
| Chemistry | 1 | 8% |
| Agricultural and Biological Sciences | 1 | 8% |
| Other | 0 | 0% |
| Unknown | 4 | 31% |
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 21 November 2010.
All research outputs
#8,533,995
of 25,371,288 outputs
Outputs from Acta Biotheoretica
#46
of 213 outputs
Outputs of similar age
#53,366
of 184,290 outputs
Outputs of similar age from Acta Biotheoretica
#1
of 4 outputs
Altmetric has tracked 25,371,288 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 213 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.2. This one has gotten more attention than average, scoring higher than 57% of its peers.
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We're also able to compare this research output to 4 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them