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Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition.
Su, Wenxia; Deng, Shuyi; Gu, Zhifeng; Yang, Keli; Ding, Hui; Chen, Hui; Zhang, Zhaoyue.
Affiliation
  • Su W; College of Science, Inner Mongolia Agriculture University, Hohhot, China.
  • Deng S; School of Life Science and Technology, Center for Information Biology, University of Electronic Science and Technology of China, Chengdu, China.
  • Gu Z; School of Life Science and Technology, Center for Information Biology, University of Electronic Science and Technology of China, Chengdu, China.
  • Yang K; Nonlinear Research Institute, Baoji University of Arts and Sciences, Baoji, China.
  • Ding H; School of Life Science and Technology, Center for Information Biology, University of Electronic Science and Technology of China, Chengdu, China.
  • Chen H; School of Healthcare Technology, Chengdu Neusoft University, Chengdu, China.
  • Zhang Z; School of Life Science and Technology, Center for Information Biology, University of Electronic Science and Technology of China, Chengdu, China.
Front Genet ; 14: 1157021, 2023.
Article in En | MEDLINE | ID: mdl-36926588
ABSTRACT

Introduction:

Apoptosis proteins play an important role in the process of cell apoptosis, which makes the rate of cell proliferation and death reach a relative balance. The function of apoptosis protein is closely related to its subcellular location, it is of great significance to study the subcellular locations of apoptosis proteins. Many efforts in bioinformatics research have been aimed at predicting their subcellular location. However, the subcellular localization of apoptotic proteins needs to be carefully studied.

Methods:

In this paper, based on amphiphilic pseudo amino acid composition and support vector machine algorithm, a new method was proposed for the prediction of apoptosis proteins\x{2019} subcellular location. Results and

Discussion:

The method achieved good performance on three data sets. The Jackknife test accuracy of the three data sets reached 90.5%, 93.9% and 84.0%, respectively. Compared with previous methods, the prediction accuracies of APACC_SVM were improved.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Front Genet Year: 2023 Document type: Article Affiliation country: China

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Risk_factors_studies Language: En Journal: Front Genet Year: 2023 Document type: Article Affiliation country: China