ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features.
ApoPred: Identification of Apolipoproteins and Their Subfamilies With Multifarious Features.
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ApoPred：具有多种特征的载脂蛋白及其亚家族的鉴定
DOI：
10.3389/fcell.2020.621144
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发表时间：
2020
影响因子：
5.5
通讯作者：
Tang H
中科院分区：
文献类型：
--
作者：
Liu T;Chen JM;Zhang D;Zhang Q;Peng B;Xu L;Tang H
Apolipoprotein is a group of plasma proteins that are associated with a variety of diseases, such as hyperlipidemia, atherosclerosis, Alzheimer’s disease, and diabetes. In order to investigate the function of apolipoproteins and to develop effective targets for related diseases, it is necessary to accurately identify and classify apolipoproteins. Although it is possible to identify apolipoproteins accurately through biochemical experiments, they are expensive and time-consuming. This work aims to establish a high-efficiency and high-accuracy prediction model for recognition of apolipoproteins and their subfamilies. We firstly constructed a high-quality benchmark dataset including 270 apolipoproteins and 535 non-apolipoproteins. Based on the dataset, pseudo-amino acid composition (PseAAC) and composition of k-spaced amino acid pairs (CKSAAP) were used as input vectors. To improve the prediction accuracy and eliminate redundant information, analysis of variance (ANOVA) was used to rank the features. And the incremental feature selection was utilized to obtain the best feature subset. Support vector machine (SVM) was proposed to construct the classification model, which could produce the accuracy of 97.27%, sensitivity of 96.30%, and specificity of 97.76% for discriminating apolipoprotein from non-apolipoprotein in 10-fold cross-validation. In addition, the same process was repeated to generate a new model for predicting apolipoprotein subfamilies. The new model could achieve an overall accuracy of 95.93% in 10-fold cross-validation. According to our proposed model, a convenient webserver called ApoPred was established, which can be freely accessed at http://tang-biolab.com/server/ApoPred/service.html. We expect that this work will contribute to apolipoprotein function research and drug development in relevant diseases.
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影响因子：
14.9
作者：
Bredesen, Bjorn Andre;Rehmsmeier, Marc
通讯作者：
Rehmsmeier, Marc
影响因子：
--
作者：
Bandarian F;Daneshpour MS;Hedayati M;Naseri M;Azizi F
通讯作者：
Azizi F
影响因子：
5.8
作者：
Hasan, Md. Mehedi;Schaduangrat, Nalini;Manavalan, Balachandran
通讯作者：
Manavalan, Balachandran
影响因子：
8.8
作者：
Basith, Shaherin;Manavalan, Balachandran;Lee, Gwang
通讯作者：
Lee, Gwang
影响因子：
--
作者：
Feng PM;Lin H;Chen W
通讯作者：
Chen W