Prediction of protein cellular attributes using pseudo‐amino acid composition

By Kuo-Chen Chou. Proteins: Structure, Function, and Bioinformatics, 2001

1,924 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7633e42149c31cea322d49e2751ad9e3c2858241

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Molecular Biology

Citations in the corpus

iEnhancer-2L: a two-layer predictor for identifying enhancers and their strength by pseudo k-tuple nucleotide composition (2016, methodology): “Actually, being evolutionarily more constrained than the underlying For the detailed mathematical formulation of SD, see Equation (4) of Chou (2001) or Equation (4) of Chou (2005).”
Subcellular location prediction of apoptosis proteins (2002, methodology): “As demonstrated in an incisive analysis by Chou, 17 because of the extremely large number of possible protein sequences, it is almost unrealistic to develop a statistical method purely based on the input of sequences without adopting any simplifying procedures.”
Impacts of bioinformatics to medicinal chemistry. (2015, methodology): “To avoid completely losing the sequence-order information for proteins, the pseudo amino acid composition [47-49]”
Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences (2015, background): “PC-PseAAC (16) is an approach incorporating the contiguous local sequence-order information and the global sequence-order information into the feature vector of the protein sequence.”
Ensemble classifier for protein fold pattern recognition (2006, methodology): “In the present study, in order to avoid completely ignoring the se-quence-order effects, the pseudo amino acid composition (Chou, 2001) was used to replace the conventional amino acid composition (Chou & Zhang, 1993; Nakashima et al., 1986) as used in (Ding & Dubchak, 2001).”
iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences (2018, methodology): “…acid composition for a protein sequence can be defines as: 20 1 1 , (1 20)cc r j r j fX c f w 20 20 1 1 , (21 20 )cc r j r j wX c f w where w is the weighting factor for the sequence-order effect and is set to w = 0.05 in iFeature as suggested by Chou et al. (Chou, 2001).”
Multitask Learning for Protein Subcellular Location Prediction (2011, methodology): “We adapt and compare two specializations of the multitask learning algorithms on 20 different organisms.”
Prediction of protein structural classes for low-homology sequences based on predicted secondary structure (2010, positive): “To overcome this limitation, various new features were developed on the basis of a so-called pseudo amino acid (PseAA) composition [12], and have been shown very successful in the prediction of protein structural classes [13,14], especially for high-homology protein datasets.”
iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. (2013, methodology): “To avoid completely losing the sequence-order information, the pseudo amino acid composition (PseAAC) was proposed, as formulated by [26]”
propy: a tool to generate various modes of Chou's PseAAC (2013, methodology): “…features have been widely used in the development of machine learning models for predicting protein structural and functional classes (Chou, 2001, 2009), protein–protein interactions (Shen et al., 2007), protein–ligand interactions (Yu et al., 2012), subcellular locations and…”
Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes (2005, methodology): “…that, according to the definition of the classical amino acid composition, all its components must be ≥0; it is not always true, however, for the pseudo amino acid composition (Chou, 2001): the components corresponding to the sequence correlation factors may also be <0, as further discussed later.”
PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. (2014, methodology): “Accordingly, it is reasonable to use the physicochemical properties of nucleotides to formulate PseKNC for DNA sequences, just like using the physicochemical properties of amino acids to formulate PseAAC [3,30] for protein sequence.”