| Title |
Identify Secretory Protein of Malaria Parasite with Modified Quadratic Discriminant Algorithm and Amino Acid Composition
|
|---|---|
| Published in |
Interdisciplinary Sciences: Computational Life Sciences, August 2015
|
| DOI | 10.1007/s12539-015-0112-0 |
| Pubmed ID | |
| Authors |
Yong-E Feng |
| Abstract |
Malaria parasite secretes various proteins in infected red blood cell for its growth and survival. Thus identification of these secretory proteins is important for developing vaccine or drug against malaria. In this study, the modified method of quadratic discriminant analysis is presented for predicting the secretory proteins. Firstly, 20 amino acids are divided into five types according to the physical and chemical characteristics of amino acids. Then, we used five types of amino acids compositions as inputs of the modified quadratic discriminant algorithm. Finally, the best prediction performance is obtained by using 20 amino acid compositions, the sensitivity of 96 %, the specificity of 92 % with 0.88 of Mathew's correlation coefficient in fivefold cross-validation test. The results are also compared with those of existing prediction methods. The compared results shown our method are prominent in the prediction of secretory proteins. |
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 % |
|---|---|---|
| Unknown | 2 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 10 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 10 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 2 | 20% |
| Student > Bachelor | 2 | 20% |
| Lecturer > Senior Lecturer | 1 | 10% |
| Student > Ph. D. Student | 1 | 10% |
| Professor > Associate Professor | 1 | 10% |
| Other | 0 | 0% |
| Unknown | 3 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 30% |
| Medicine and Dentistry | 3 | 30% |
| Agricultural and Biological Sciences | 1 | 10% |
| Unknown | 3 | 30% |
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 20 August 2015.
All research outputs
#17,770,433
of 22,824,164 outputs
Outputs from Interdisciplinary Sciences: Computational Life Sciences
#147
of 294 outputs
Outputs of similar age
#179,460
of 266,176 outputs
Outputs of similar age from Interdisciplinary Sciences: Computational Life Sciences
#11
of 26 outputs
Altmetric has tracked 22,824,164 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 294 research outputs from this source. They receive a mean Attention Score of 2.9. This one is in the 44th percentile – i.e., 44% 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 266,176 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 27th percentile – i.e., 27% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 26 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 57% of its contemporaries.