PseAAC-Builder

PseAAC-Builder: a cross-platform stand-alone program for generating various special Chou’s pseudo-amino acid compositions. The pseudo-amino acid composition has been widely used to convert complicated protein sequences with various lengths to fixed length digital feature vectors while keeping considerable sequence order information. However, so far the only software available to the public is the web server PseAAC (http://www.csbio.sjtu.edu.cn/bioinf/PseAAC), which has some limitations in dealing with large-scale datasets. Here, we propose a new cross-platform stand-alone software program, called PseAAC-Builder (http://www.pseb.sf.net), which can be used to generate various modes of Chou’s pseudo-amino acid composition in a much more efficient and flexible way. It is anticipated that PseAAC-Builder may become a useful tool for studying various protein attributes


References in zbMATH (referenced in 25 articles )

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  1. Marrero-Ponce, Yovani; Teran, Julio E.; Contreras-Torres, Ernesto; García-Jacas, César R.; Perez-Castillo, Yunierkis; Cubillan, Nestor; Peréz-Giménez, Facundo; Valdés-Martini, José R.: LEGO-based generalized set of two linear algebraic 3D bio-macro-molecular descriptors: theory and validation by QSARs (2020)
  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. Tahir, Muhammad; Tayara, Hilal; Chong, Kil To: iRNA-PseKNC(2methyl): identify RNA 2’-O-methylation sites by convolution neural network and Chou’s pseudo components (2019)
  4. Zhao, Wei; Li, Guang-Ping; Wang, Jun; Zhou, Yuan-Ke; Gao, Yang; Du, Pu-Feng: Predicting protein sub-Golgi locations by combining functional domain enrichment scores with pseudo-amino acid compositions (2019)
  5. 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)
  6. Srivastava, Abhishikha; Kumar, Ravindra; Kumar, Manish: BlaPred: predicting and classifying (\beta)-lactamase using a 3-tier prediction system via Chou’s general PseAAC (2018)
  7. Ali, Farman; Hayat, Maqsood: Machine learning approaches for discrimination of extracellular matrix proteins using hybrid feature space (2016)
  8. 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)
  9. 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)
  10. Jiao, Ya-Sen; Du, Pu-Feng: Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (2016)
  11. Ali, Farman; Hayat, Maqsood: Classification of membrane protein types using voting feature interval in combination with Chou’s pseudo amino acid composition (2015)
  12. Ju, Zhe; Cao, Jun-Zhe; Gu, Hong: iLM-2L: a two-level predictor for identifying protein lysine methylation sites and their methylation degrees by incorporating K-gap amino acid pairs into Chou’s general PseAAC (2015)
  13. Khan, Zaheer Ullah; Hayat, Maqsood; Khan, Muazzam Ali: Discrimination of acidic and alkaline enzyme using Chou’s pseudo amino acid composition in conjunction with probabilistic neural network model (2015)
  14. 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)
  15. 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)
  16. Hajisharifi, Zohre; Piryaiee, Moien; Mohammad Beigi, Majid; Behbahani, Mandana; Mohabatkar, Hassan: Predicting anticancer peptides with Chou’s pseudo amino acid composition and investigating their mutagenicity via ames test (2014)
  17. 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)
  18. Mondal, Sukanta; Pai, Priyadarshini P.: Chou’s pseudo amino acid composition improves sequence-based antifreeze protein prediction (2014)
  19. Nanni, Loris; Lumini, Alessandra; Brahnam, Sheryl: A set of descriptors for identifying the protein-drug interaction in cellular networking (2014)
  20. 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)

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