iLoc-Euk: a multi-label classifier for predicting the subcellular localization of singleplex and multiplex eukaryotic proteins. Predicting protein subcellular localization is an important and difficult problem, particularly when query proteins may have the multiplex character, i.e., simultaneously exist at, or move between, two or more different subcellular location sites. Most of the existing protein subcellular location predictor can only be used to deal with the single-location or ”singleplex” proteins. Actually, multiple-location or ”multiplex” proteins should not be ignored because they usually posses some unique biological functions worthy of our special notice. By introducing the ”multi-labeled learning” and ”accumulation-layer scale”, a new predictor, called iLoc-Euk, has been developed that can be used to deal with the systems containing both singleplex and multiplex proteins. As a demonstration, the jackknife cross-validation was performed with iLoc-Euk on a benchmark dataset of eukaryotic proteins classified into the following 22 location sites: (1) acrosome, (2) cell membrane, (3) cell wall, (4) centriole, (5) chloroplast, (6) cyanelle, (7) cytoplasm, (8) cytoskeleton, (9) endoplasmic reticulum, (10) endosome, (11) extracellular, (12) Golgi apparatus, (13) hydrogenosome, (14) lysosome, (15) melanosome, (16) microsome (17) mitochondrion, (18) nucleus, (19) peroxisome, (20) spindle pole body, (21) synapse, and (22) vacuole, where none of proteins included has ≥25% pairwise sequence identity to any other in a same subset. The overall success rate thus obtained by iLoc-Euk was 79%, which is significantly higher than that by any of the existing predictors that also have the capacity to deal with such a complicated and stringent system. As a user-friendly web-server, iLoc-Euk is freely accessible to the public at the web-site It is anticipated that iLoc-Euk may become a useful bioinformatics tool for Molecular Cell Biology, Proteomics, System Biology, and Drug Development Also, its novel approach will further stimulate the development of predicting other protein attributes.

References in zbMATH (referenced in 37 articles )

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  1. Shen, Yinan; Tang, Jijun; Guo, Fei: Identification of protein subcellular localization via integrating evolutionary and physicochemical information into Chou’s general PseAAC (2019)
  2. Akbar, Shahid; Hayat, Maqsood: iMethyl-STTNC: identification of N(^6)-methyladenosine sites by extending the idea of SAAC into Chou’s PseAAC to formulate RNA sequences (2018)
  3. Mei, Juan; Fu, Yi; Zhao, Ji: Analysis and prediction of ion channel inhibitors by using feature selection and Chou’s general pseudo amino acid composition (2018)
  4. Zhang, Shengli; Duan, Xin: Prediction of protein subcellular localization with oversampling approach and Chou’s general PseAAC (2018)
  5. Zhang, Shengli; Liang, Yunyun: Predicting apoptosis protein subcellular localization by integrating auto-cross correlation and PSSM into Chou’s PseAAC (2018)
  6. Jiao, Ya-Sen; Du, Pu-Feng: Prediction of Golgi-resident protein types using general form of Chou’s pseudo-amino acid compositions: approaches with minimal redundancy maximal relevance feature selection (2016)
  7. Jiao, Ya-Sen; Du, Pu-Feng: Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (2016)
  8. Muthu Krishnan, S.: Classify vertebrate hemoglobin proteins by incorporating the evolutionary information into the general PseAAC with the hybrid approach (2016)
  9. Fu, Haoyue; Yang, Lianping; Zhang, Xiangde: An RNA secondary structure prediction method based on minimum and suboptimal free energy structures (2015)
  10. Kumar, Ravindra; Srivastava, Abhishikha; Kumari, Bandana; Kumar, Manish: Prediction of (\beta)-lactamase and its class by Chou’s pseudo-amino acid composition and support vector machine (2015)
  11. Han, Guo-Sheng; Yu, Zu-Guo; Anh, Vo: A two-stage SVM method to predict membrane protein types by incorporating amino acid classifications and physicochemical properties into a general form of Chou’s PseAAC (2014)
  12. Lyons, James; Biswas, Neela; Sharma, Alok; Dehzangi, Abdollah; Paliwal, Kuldip K.: Protein fold recognition by alignment of amino acid residues using kernelized dynamic time warping (2014)
  13. Mei, Suyu: \textitSVMensemble based transfer learning for large-scale membrane proteins discrimination (2014)
  14. Tahir, Muhammad; Khan, Asifullah; Kaya, Hüseyin: Protein subcellular localization in human and hamster cell lines: employing local ternary patterns of fluorescence microscopy images (2014)
  15. Yang, Lei; Lv, Yingli; Li, Tao; Zuo, Yongchun; Jiang, Wei: Human proteins characterization with subcellular localizations (2014)
  16. Fan, Guo-Liang; Li, Qian-Zhong: Discriminating bioluminescent proteins by incorporating average chemical shift and evolutionary information into the general form of Chou’s pseudo amino acid composition (2013)
  17. Feng, Peng-Mian; Ding, Hui; Chen, Wei; Lin, Hao: Naïve Bayes classifier with feature selection to identify phage virion proteins (2013)
  18. Huang, Chao; Yuan, Jing-Qi: Predicting protein subchloroplast locations with both single and multiple sites via three different modes of Chou’s pseudo amino acid compositions (2013)
  19. Xiao, Xuan; Min, Jian-Liang; Wang, Pu; Chou, Kuo-Chen: iCDI-PseFpt: identify the channel-drug interaction in cellular networking with PseAAC and molecular fingerprints (2013)
  20. Zhou, Xuan; Li, Zhanchao; Dai, Zong; Zou, Xiaoyong: Predicting promoters by pseudo-trinucleotide compositions based on discrete wavelets transform (2013)

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