PseAAC

PseAAC: A flexible web server for generating various kinds of protein pseudo amino acid composition. The pseudo amino acid (PseAA) composition can represent a protein sequence in a discrete model without completely losing its sequence-order information, and hence has been widely applied for improving the prediction quality for various protein attributes. However, dealing with different problems may need different kinds of PseAA composition. Here, we present a web-server called PseAAC at http://chou.med.harvard.edu/bioinf/PseAA/, by which users can generate various kinds of PseAA composition to best fit their need.


References in zbMATH (referenced in 30 articles )

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  1. 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)
  2. 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)
  3. 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)
  4. Srivastava, Abhishikha; Kumar, Ravindra; Kumar, Manish: BlaPred: predicting and classifying (\beta)-lactamase using a 3-tier prediction system via Chou’s general PseAAC (2018)
  5. Ali, Farman; Hayat, Maqsood: Machine learning approaches for discrimination of extracellular matrix proteins using hybrid feature space (2016)
  6. Jiao, Ya-Sen; Du, Pu-Feng: Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (2016)
  7. 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)
  8. Ali, Farman; Hayat, Maqsood: Classification of membrane protein types using voting feature interval in combination with Chou’s pseudo amino acid composition (2015)
  9. Kou, Gaoshan; Feng, Yonge: Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts (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. Bakhtiarizadeh, Mohammad Reza; Moradi-Shahrbabak, Mohammad; Ebrahimi, Mansour; Ebrahimie, Esmaeil: Neural network and SVM classifiers accurately predict lipid binding proteins, irrespective of sequence homology (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. Mondal, Sukanta; Pai, Priyadarshini P.: Chou’s pseudo amino acid composition improves sequence-based antifreeze protein prediction (2014)
  14. Nanni, Loris; Brahnam, Sheryl; Lumini, Alessandra: Prediction of protein structure classes by incorporating different protein descriptors into general Chou’s pseudo amino acid composition (2014)
  15. Nanni, Loris; Lumini, Alessandra; Brahnam, Sheryl: A set of descriptors for identifying the protein-drug interaction in cellular networking (2014)
  16. Chen, Yen-Kuang; Li, Kuo-Bin: Predicting membrane protein types by incorporating protein topology, domains, signal peptides, and physicochemical properties into the general form of Chou’s pseudo amino acid composition (2013)
  17. 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)
  18. 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)
  19. Chou, Kuo-Chen: Some remarks on protein attribute prediction and pseudo amino acid composition (2011)
  20. González-Díaz, Humberto; Prado-Prado, Francisco; Sobarzo-Sánchez, Eduardo; Haddad, Mohamed; Maurel Chevalley, Séverine; Valentin, Alexis; Quetin-Leclercq, Joëlle; Dea-Ayuela, María A.; Gomez-Muños, María Teresa; Munteanu, Cristian R.; Torres-Labandeira, Juan José; García-Mera, Xerardo; Tapia, Ricardo A.; Ubeira, Florencio M.: NL MIND-BEST: a web server for ligands and proteins discovery -- theoretic-experimental study of proteins of \textitGiardialamblia and new compounds active against \textitPlasmodiumfalciparum (2011)

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