iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. A cell contains thousands of proteins. Many important functions of cell are carried out through the proteins therein. Proteins rarely function alone. Most of their functions essential to life are associated with various types of protein-protein interactions (PPIs). Therefore, knowledge of PPIs is fundamental for both basic research and drug development. With the avalanche of proteins sequences generated in the postgenomic age, it is highly desired to develop computational methods for timely acquiring this kind of knowledge. Here, a new predictor, called ”iPPI-Emsl”, is developed. In the predictor, a protein sample is formulated by incorporating the following two types of information into the general form of PseAAC (pseudo amino acid composition): (1) the physicochemical properties derived from the constituent amino acids of a protein; and (2) the wavelet transforms derived from the numerical series along a protein chain. The operation engine to run the predictor is an ensemble classifier formed by fusing seven individual random forest engines via a voting system. It is demonstrated with the benchmark dataset from Saccharomyces cerevisiae as well as the dataset from Helicobacter pylori that the new predictor achieves remarkably higher success rates than any of the existing predictors in this area. The new predictor׳ web-server has been established at http://www.jci-bioinfo.cn/iPPI-Esml. For the convenience of most experimental scientists, we have further provided a step-by-step guide, by which users can easily get their desired results without the need to follow the complicated mathematics involved during its development

References in zbMATH (referenced in 25 articles )

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  1. Adilina, Sheikh; Farid, Dewan Md; Shatabda, Swakkhar: Effective DNA binding protein prediction by using key features via Chou’s general PseAAC (2019)
  2. Ahmad, Jamal; Hayat, Maqsood: MFSC: multi-voting based feature selection for classification of Golgi proteins by adopting the general form of Chou’s PseAAC components (2019)
  3. Jia, Jianhua; Li, Xiaoyan; Qiu, Wangren; Xiao, Xuan; Chou, Kuo-Chen: iPPI-PseAAC(CGR): identify protein-protein interactions by incorporating chaos game representation into PseAAC (2019)
  4. Tian, Baoguang; Wu, Xue; Chen, Cheng; Qiu, Wenying; Ma, Qin; Yu, Bin: Predicting protein-protein interactions by fusing various Chou’s pseudo components and using wavelet denoising approach (2019)
  5. Wang, Lidong; Zhang, Ruijun; Mu, Yashuang: Fu-SulfPred: identification of protein S-sulfenylation sites by fusing forests via Chou’s general PseAAC (2019)
  6. 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)
  7. Arif, Muhammad; Hayat, Maqsood; Jan, Zahoor: IMem-2LSAAC: a two-level model for discrimination of membrane proteins and their types by extending the notion of SAAC into Chou’s pseudo amino acid composition (2018)
  8. Cheng, Xiang; Xiao, Xuan; Chou, Kuo-Chen: pLoc(_-)bal-mGneg: predict subcellular localization of Gram-negative bacterial proteins by quasi-balancing training dataset and general PseAAC (2018)
  9. Contreras-Torres, Ernesto: Predicting structural classes of proteins by incorporating their global and local physicochemical and conformational properties into general Chou’s PseAAC (2018)
  10. Liang, Yunyun; Zhang, Shengli: Identify Gram-negative bacterial secreted protein types by incorporating different modes of PSSM into Chou’s general PseAAC via Kullback-Leibler divergence (2018)
  11. 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)
  12. Qiu, Wenying; Li, Shan; Cui, Xiaowen; Yu, Zhaomin; Wang, Minghui; Du, Junwei; Peng, Yanjun; Yu, Bin: Predicting protein submitochondrial locations by incorporating the pseudo-position specific scoring matrix into the general Chou’s pseudo-amino acid composition (2018)
  13. Sabooh, M. Fazli; Iqbal, Nadeem; Khan, Mukhtaj; Khan, Muslim; Maqbool, H. F.: Identifying 5-methylcytosine sites in RNA sequence using composite encoding feature into Chou’s PseKNC (2018)
  14. Srivastava, Abhishikha; Kumar, Ravindra; Kumar, Manish: BlaPred: predicting and classifying (\beta)-lactamase using a 3-tier prediction system via Chou’s general PseAAC (2018)
  15. Jiao, Xiong; Ranganathan, Shoba: Prediction of interface residue based on the features of residue interaction network (2017)
  16. Saghapour, Ehsan; Sehhati, Mohammadreza: Prediction of metastasis in advanced colorectal carcinomas using CGH data (2017)
  17. Zhai, Jing-Xuan; Cao, Tian-Jie; An, Ji-Yong; Bian, Yong-Tao: Highly accurate prediction of protein self-interactions by incorporating the average block and PSSM information into the general PseAAC (2017)
  18. Ali, Farman; Hayat, Maqsood: Machine learning approaches for discrimination of extracellular matrix proteins using hybrid feature space (2016)
  19. Jia, Jianhua; Liu, Zi; Xiao, Xuan; Liu, Bingxiang; Chou, Kuo-Chen: pSuc-Lys: predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach (2016)
  20. Jiao, Ya-Sen; Du, Pu-Feng: Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (2016)

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