Literature DB >> 30179658

pLoc_bal-mHum: Predict subcellular localization of human proteins by PseAAC and quasi-balancing training dataset.

Kuo-Chen Chou1, Xiang Cheng2, Xuan Xiao3.   

Abstract

A cell contains numerous protein molecules. One of the fundamental goals in molecular cell biology is to determine their subcellular locations since this information is extremely important to both basic research and drug development. In this paper, we report a novel and very powerful predictor called "pLoc_bal-mHum" for predicting the subcellular localization of human proteins based on their sequence information alone. Cross-validation tests on exactly the same experiment-confirmed dataset have indicated that the new predictor is remarkably superior to the existing state-of-the-art predictor in identifying the subcellular localization of human proteins. To maximize the convenience for the majority of experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mHum/, by which users can easily get their desired results without the need to go through the detailed mathematics.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  5-step rules; A set of 5 intuitive metrics for multi-label predictors; Human proteins; IHTS treatment; Multi-label system; Quasi-balance

Mesh:

Substances:

Year:  2018        PMID: 30179658     DOI: 10.1016/j.ygeno.2018.08.007

Source DB:  PubMed          Journal:  Genomics        ISSN: 0888-7543            Impact factor:   5.736


  12 in total

1.  iPhosY-PseAAC: identify phosphotyrosine sites by incorporating sequence statistical moments into PseAAC.

Authors:  Yaser Daanial Khan; Nouman Rasool; Waqar Hussain; Sher Afzal Khan; Kuo-Chen Chou
Journal:  Mol Biol Rep       Date:  2018-10-11       Impact factor: 2.316

Review 2.  Structural Variability in the RLR-MAVS Pathway and Sensitive Detection of Viral RNAs.

Authors:  Qiu-Xing Jiang
Journal:  Med Chem       Date:  2019       Impact factor: 2.745

Review 3.  Some illuminating remarks on molecular genetics and genomics as well as drug development.

Authors:  Kuo-Chen Chou
Journal:  Mol Genet Genomics       Date:  2020-01-01       Impact factor: 3.291

4.  Prediction of DNA binding proteins using local features and long-term dependencies with primary sequences based on deep learning.

Authors:  Guobin Li; Xiuquan Du; Xinlu Li; Le Zou; Guanhong Zhang; Zhize Wu
Journal:  PeerJ       Date:  2021-05-03       Impact factor: 2.984

5.  Identify Lysine Neddylation Sites Using Bi-profile Bayes Feature Extraction via the Chou's 5-steps Rule and General Pseudo Components.

Authors:  Zhe Ju; Shi-Yun Wang
Journal:  Curr Genomics       Date:  2019-12       Impact factor: 2.236

6.  iHyd-PseAAC (EPSV): Identifying Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant Feature via Chou's 5-Step Rule and General Pseudo Amino Acid Composition.

Authors:  Asma Ehsan; Muhammad K Mahmood; Yaser D Khan; Omar M Barukab; Sher A Khan; Kuo-Chen Chou
Journal:  Curr Genomics       Date:  2019-02       Impact factor: 2.236

7.  RAACBook: a web server of reduced amino acid alphabet for sequence-dependent inference by using Chou's five-step rule.

Authors:  Lei Zheng; Shenghui Huang; Nengjiang Mu; Haoyue Zhang; Jiayu Zhang; Yu Chang; Lei Yang; Yongchun Zuo
Journal:  Database (Oxford)       Date:  2019-01-01       Impact factor: 3.451

8.  iMethylK_pseAAC: Improving Accuracy of Lysine Methylation Sites Identification by Incorporating Statistical Moments and Position Relative Features into General PseAAC via Chou's 5-steps Rule.

Authors:  Sarah Ilyas; Waqar Hussain; Adeel Ashraf; Yaser Daanial Khan; Sher Afzal Khan; Kuo-Chen Chou
Journal:  Curr Genomics       Date:  2019-05       Impact factor: 2.236

9.  iSulfoTyr-PseAAC: Identify Tyrosine Sulfation Sites by Incorporating Statistical Moments via Chou's 5-steps Rule and Pseudo Components.

Authors:  Omar Barukab; Yaser Daanial Khan; Sher Afzal Khan; Kuo-Chen Chou
Journal:  Curr Genomics       Date:  2019-05       Impact factor: 2.236

10.  Characterization of the relationship between FLI1 and immune infiltrate level in tumour immune microenvironment for breast cancer.

Authors:  Shiyuan Wang; Yakun Wang; Chunlu Yu; Yiyin Cao; Yao Yu; Yi Pan; Dongqing Su; Qianzi Lu; Wuritu Yang; Yongchun Zuo; Lei Yang
Journal:  J Cell Mol Med       Date:  2020-04-05       Impact factor: 5.310

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