The pLoc_bal-mGneg Predictor is a Powerful Web-Server for Identifying the Subcellular Localization of Gram-Negative Bacterial Proteins based on their Sequences Information Alone

Chou, Kuo‐Chen

doi:10.18483/ijsci.2248

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…According to a series of recent publications [49,50], user-friendly and publicly accessible web-servers make great importance in building predictive system. We will make great efforts to provide a web-server for the proposed method in our future work.…”

Section: Discussionmentioning

confidence: 99%

Subcellular location prediction of apoptosis proteins using two novel feature extraction methods based on evolutionary information and LDA

Meng

Chen

et al. 2020

BMC Bioinformatics

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Background Apoptosis, also called programmed cell death, refers to the spontaneous and orderly death of cells controlled by genes in order to maintain a stable internal environment. Identifying the subcellular location of apoptosis proteins is very helpful in understanding the mechanism of apoptosis and designing drugs. Therefore, the subcellular localization of apoptosis proteins has attracted increased attention in computational biology. Effective feature extraction methods play a critical role in predicting the subcellular location of proteins. Results In this paper, we proposed two novel feature extraction methods based on evolutionary information. One of the features obtained the evolutionary information via the transition matrix of the consensus sequence (CTM). And the other utilized the evolutionary information from PSSM based on absolute entropy correlation analysis (AECA-PSSM). After fusing the two kinds of features, linear discriminant analysis (LDA) was used to reduce the dimension of the proposed features. Finally, the support vector machine (SVM) was adopted to predict the protein subcellular locations. The proposed CTM-AECA-PSSM-LDA subcellular location prediction method was evaluated using the CL317 dataset and ZW225 dataset. By jackknife test, the overall accuracy was 99.7% (CL317) and 95.6% (ZW225) respectively. Conclusions The experimental results show that the proposed method which is hopefully to be a complementary tool for the existing methods of subcellular localization, can effectively extract more abundant features of protein sequence and is feasible in predicting the subcellular location of apoptosis proteins.

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Section: Discussionmentioning

confidence: 99%

Subcellular location prediction of apoptosis proteins using two novel feature extraction methods based on evolutionary information and LDA

Meng

Chen

et al. 2020

BMC Bioinformatics

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“…impressive papers [35][36][37][38][39][40][41] and a series of very recent papers (see, e.g., [42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58][59]).…”

Section: Natural Sciencementioning

confidence: 99%

Proposing 5-Steps Rule Is a Notable Milestone for Studying Molecular Biology

Chou¹

2020

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“…Although at the present the reports in this regard from theoretical scientists are more than those from experimental scientists, it is anticipated that, with more experimental data available in future, this kind of reports from experimental scientists will be increasing as well. Particularly, the combined reports between experimental and theoretical approaches, or their compliments to each other, will increasingly appear, as indicated by some very impressive papers [35][36][37][38][39][40][41] and a series of very recent papers (see, e.g., [42][43][44][45][46][47][48][49][50][51][52][53][54][55][56]).…”

Section: Conclusion and Perspectivementioning

confidence: 99%