Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition
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...
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Published in | Frontiers in genetics Vol. 14; p. 1157021 |
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Main Authors | , , , , , , |
Format | Journal Article |
Language | English |
Published |
Switzerland
Frontiers Media S.A
28.02.2023
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Subjects | |
Online Access | Get full text |
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Summary: | 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.
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.
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. |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 This article was submitted to Computational Genomics, a section of the journal Frontiers in Genetics Edited by: Lin Zhang, China University of Mining and Technology, China Shaherin Basith, Ajou University, Republic of Korea Reviewed by: Lei Yang, Harbin Medical University, China |
ISSN: | 1664-8021 1664-8021 |
DOI: | 10.3389/fgene.2023.1157021 |