Pseudo Amino Acid Composition and its Applications in Bioinformatics, Proteomics and System Biology

By Kuo-Chen Chou. 2009

465 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/e8db4c01d32cad0694852a94052c1b7d88842ef6

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

1. Kuo-Chen Chou
2. Lukasz Kurgan
3. Hongbin Shen
4. Xuan Xiao
5. Dongsheng Cao
6. Xin Gao
7. Jianhua Jia
8. Yu Li

Citations in the corpus

• propy: a tool to generate various modes of Chou's PseAAC (2013, methodology): “The usefulness of the features covered by propy for computing the structural and physicochemical features of proteins and peptides has been validated by a number of published studies (Chou, 2009, 2011).”
• iSNO-PseAAC: Predict Cysteine S-Nitrosylation Sites in Proteins by Incorporating Position Specific Amino Acid Propensity into Pseudo Amino Acid Composition (2013, methodology): “The idea of PseAAC has been widely used in bioinformatics, proteomics, and system biology [25], such as predicting protein structural class [31], predicting metalloproteinase family [32], predicting protein subcellular localization [33], predicting DNA-binding proteins [21], identifying allergenic p”
• A New Method for Predicting the Subcellular Localization of Eukaryotic Proteins with Both Single and Multiple Sites: Euk-mPLoc 2.0 (2010, unknown): “, [21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42] and a long list of PseAAC-related references cited in a recent review [20]).”
• pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. (2016, methodology): “To avoid this, the pseudo amino acid composition [12; 35] or PseAAC [36] was proposed.”
• iFC2: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content (2010, methodology): “Another emerging descriptor of protein sequence is the pseudo amino acid composition, which comes in multiple forms and which was recently used to predict a number of different protein attributes ( Chou 2001, 2005, 2009 ).”
• DEEPre: sequence-based enzyme EC number prediction by deep learning (2017, methodology): “Pse-PSSM is developed from the pseudo amino acid, a highly innovative manually designed feature which has already been proved successful in a number of problems (Chou, 2009; Hayat and Khan, 2012).”
• 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]”
• Deep learning in bioinformatics: introduction, application, and perspective in big data era (2019, background): “nt neural networks [103] ResNet Residual networks [52] LSTM Long short-term memory [44] MRI Magnetic resonance imaging [133] PET Positron emission tomography [84] PseAAC Pseudo amino acid composition [20] PSSM Position specific scoring matrix [85] ReLU Rectified linear unit [108] SENet Squeeze-and-exc”
• Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization (2010, background): “Accordingly, it is essentially through the concept of PseAAC [15] that the GO information, FunD information, and SeqEvo information have been effectively incorporated into the predictor Plant-mPLoc.”
• iNuc-PseKNC: a sequence-based predictor for predicting nucleosome positioning in genomes with pseudo k-tuple nucleotide composition (2014, unknown): “3 of (Chou, 2009), the PseKNC”