| Title |
APSLAP: An Adaptive Boosting Technique for Predicting Subcellular Localization of Apoptosis Protein
|
|---|---|
| Published in |
Acta Biotheoretica, August 2013
|
| DOI | 10.1007/s10441-013-9197-1 |
| Pubmed ID | |
| Authors |
Vijayakumar Saravanan, P. T. V. Lakshmi |
| Abstract |
Apoptotic proteins play key roles in understanding the mechanism of programmed cell death. Knowledge about the subcellular localization of apoptotic protein is constructive in understanding the mechanism of programmed cell death, determining the functional characterization of the protein, screening candidates in drug design, and selecting protein for relevant studies. It is also proclaimed that the information required for determining the subcellular localization of protein resides in their corresponding amino acid sequence. In this work, a new biological feature, class pattern frequency of physiochemical descriptor, was effectively used in accordance with the amino acid composition, protein similarity measure, CTD (composition, translation, and distribution) of physiochemical descriptors, and sequence similarity to predict the subcellular localization of apoptosis protein. AdaBoost with the weak learner as Random-Forest was designed for the five modules and prediction is made based on the weighted voting system. Bench mark dataset of 317 apoptosis proteins were subjected to prediction by our system and the accuracy was found to be 100.0 and 92.4 %, and 90.1 % for self-consistency test, jack-knife test, and tenfold cross validation test respectively, which is 0.9 % higher than that of other existing methods. Beside this, the independent data (N151 and ZW98) set prediction resulted in the accuracy of 90.7 and 87.7 %, respectively. These results show that the protein feature represented by a combined feature vector along with AdaBoost algorithm holds well in effective prediction of subcellular localization of apoptosis proteins. The user friendly web interface "APSLAP" has been constructed, which is freely available at http://apslap.bicpu.edu.in and it is anticipated that this tool will play a significant role in determining the specific role of apoptosis proteins with reliability. |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 14 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 4 | 29% |
| Student > Master | 3 | 21% |
| Student > Doctoral Student | 2 | 14% |
| Professor > Associate Professor | 2 | 14% |
| Researcher | 2 | 14% |
| Other | 1 | 7% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 57% |
| Biochemistry, Genetics and Molecular Biology | 4 | 29% |
| Philosophy | 1 | 7% |
| Engineering | 1 | 7% |
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 31 December 2016.
All research outputs
#8,533,995
of 25,371,288 outputs
Outputs from Acta Biotheoretica
#46
of 213 outputs
Outputs of similar age
#71,902
of 212,157 outputs
Outputs of similar age from Acta Biotheoretica
#1
of 3 outputs
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