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 8 articles )

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  1. Ali, Farman; Hayat, Maqsood: Machine learning approaches for discrimination of extracellular matrix proteins using hybrid feature space (2016)
  2. Jiao, Ya-Sen; Du, Pu-Feng: Predicting Golgi-resident protein types using pseudo amino acid compositions: approaches with positional specific physicochemical properties (2016)
  3. 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)
  4. Ali, Farman; Hayat, Maqsood: Classification of membrane protein types using voting feature interval in combination with Chou’s pseudo amino acid composition (2015)
  5. Kou, Gaoshan; Feng, Yonge: Identify five kinds of simple super-secondary structures with quadratic discriminant algorithm based on the chemical shifts (2015)
  6. 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)
  7. 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)
  8. Zakeri, Pooya; Moshiri, Behzad; Sadeghi, Mehdi: Prediction of protein submitochondria locations based on data fusion of various features of sequences (2011)