Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction

doi:10.1186/s12864-018-4849-9

	Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction
	Yu,Bin 1,2,3; Li,Shan 1,2; Qiu,Wenying 1,2; Wang,Minghui 1,2; Du,Junwei 4; Zhang,Yusen 5; Chen,Xing 6
刊名	BMC Genomics
	2018-06-19
卷号	19 期号:1
关键词	Apoptosis proteins Subcellular localization Pseudo-position specific scoring matrix Detrended cross-correlation analysis coefficient Local fisher discriminant analysis Support vector machine
ISSN号	1471-2164
DOI	10.1186/s12864-018-4849-9
英文摘要	AbstractBackgroundApoptosis is associated with some human diseases, including cancer, autoimmune disease, neurodegenerative disease and ischemic damage, etc. Apoptosis proteins subcellular localization information is very important for understanding the mechanism of programmed cell death and the development of drugs. Therefore, the prediction of subcellular localization of apoptosis protein is still a challenging task.ResultsIn this paper, we propose a novel method for predicting apoptosis protein subcellular localization, called PsePSSM-DCCA-LFDA. Firstly, the protein sequences are extracted by combining pseudo-position specific scoring matrix (PsePSSM) and detrended cross-correlation analysis coefficient (DCCA coefficient), then the extracted feature information is reduced dimensionality by LFDA (local Fisher discriminant analysis). Finally, the optimal feature vectors are input to the SVM classifier to predict subcellular location of the apoptosis proteins. The overall prediction accuracy of 99.7, 99.6 and 100% are achieved respectively on the three benchmark datasets by the most rigorous jackknife test, which is better than other state-of-the-art methods.ConclusionThe experimental results indicate that our method can significantly improve the prediction accuracy of subcellular localization of apoptosis proteins, which is quite high to be able to become a promising tool for further proteomics studies. The source code and all datasets are available at https://github.com/QUST-BSBRC/PsePSSM-DCCA-LFDA/.
语种	英语
出版者	BioMed Central
WOS记录号	BMC:10.1186/S12864-018-4849-9
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/422]
专题	中国科学院数学与系统科学研究院
通讯作者	Yu,Bin
作者单位	1. 2. 3. 4. 5. 6.
推荐引用方式 GB/T 7714	Yu,Bin,Li,Shan,Qiu,Wenying,et al. Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction[J]. BMC Genomics,2018,19(1).
APA	Yu,Bin.,Li,Shan.,Qiu,Wenying.,Wang,Minghui.,Du,Junwei.,...&Chen,Xing.(2018).Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction.BMC Genomics,19(1).
MLA	Yu,Bin,et al."Prediction of subcellular location of apoptosis proteins by incorporating PsePSSM and DCCA coefficient based on LFDA dimensionality reduction".BMC Genomics 19.1(2018).