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Attention Score in Context
| Title |
Subcellular localization prediction of apoptosis proteins based on evolutionary information and support vector machine
|
|---|---|
| Published in |
Artificial Intelligence in Medicine, May 2017
|
| DOI | 10.1016/j.artmed.2017.05.007 |
| Pubmed ID | |
| Authors |
Qilin Xiang, Bo Liao, Xianhong Li, Huimin Xu, Jing Chen, Zhuoxing Shi, Qi Dai, Yuhua Yao |
| Abstract |
In this paper, a high-quality sequence encoding scheme is proposed for predicting subcellular location of apoptosis proteins. In the proposed methodology, the novel evolutionary-conservative information is introduced to represent protein sequences. Meanwhile, based on the proportion of golden section in mathematics, position-specific scoring matrix (PSSM) is divided into several blocks. Then, these features are predicted by support vector machine (SVM) and the predictive capability of proposed method is implemented by jackknife test RESULTS: The results show that the golden section method is better than no segmentation method. The overall accuracy for ZD98 and CL317 is 98.98% and 91.11%, respectively, which indicates that our method can play a complimentary role to the existing methods in the relevant areas. The proposed feature representation is powerful and the prediction accuracy will be improved greatly, which denotes our method provides the state-of-the-art performance for predicting subcellular location of apoptosis proteins. |
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 % |
|---|---|---|
| Italy | 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 23 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 4 | 17% |
| Student > Doctoral Student | 3 | 13% |
| Student > Ph. D. Student | 3 | 13% |
| Student > Master | 3 | 13% |
| Researcher | 2 | 9% |
| Other | 5 | 22% |
| Unknown | 3 | 13% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 7 | 30% |
| Engineering | 4 | 17% |
| Medicine and Dentistry | 3 | 13% |
| Mathematics | 1 | 4% |
| Philosophy | 1 | 4% |
| Other | 2 | 9% |
| Unknown | 5 | 22% |
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 23 May 2017.
All research outputs
#20,660,571
of 25,382,440 outputs
Outputs from Artificial Intelligence in Medicine
#712
of 913 outputs
Outputs of similar age
#251,707
of 327,119 outputs
Outputs of similar age from Artificial Intelligence in Medicine
#9
of 16 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. This one is in the 10th percentile – i.e., 10% of other outputs scored the same or lower than it.
So far Altmetric has tracked 913 research outputs from this source. They receive a mean Attention Score of 4.8. This one is in the 9th percentile – i.e., 9% of its peers scored the same or lower than it.
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We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.