PseAAC: a flexible web server for generating various kinds of protein pseudo amino acid composition.

By Hongbin Shen, Kuo-Chen Chou. Analytical Biochemistry, 2008

437 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/90966ab7819dc22f1dea41c35d99e4da50a4c8e7

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Citations in the corpus

A New Method for Predicting the Subcellular Localization of Eukaryotic Proteins with Both Single and Multiple Sites: Euk-mPLoc 2.0 (2010, unknown): “(3) Although the PseAAC approach, a complement to the GO approach in Euk-mPLoc, can take into account some partial sequence order effects, the original PseAAC [16] [19] missed the functional domain and sequential evolution information that may considerably affect the prediction quality.”
Pseudo Amino Acid Composition and its Applications in Bioinformatics, Proteomics and System Biology (2009, methodology): “Owing to the wide usage of PseAA composition, a webserver called “PseAAC” [162] was established at the website http://chou.”
The lncLocator: a subcellular localization predictor for long non‐coding RNAs based on a stacked ensemble classifier (2018, unknown): “…the well-established predictors usually utilize multiple types of features, including amino acid statistical attributes (Park and Kanehisa, 2003; Shen and Chou, 2008), signal peptide (Savojardo et al., 2015), functional domain (Marchler-Bauer et al., 2005), gene annotation (Chou and Cai, 2003;…”
Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms (2008, methodology): “Because PseAAC has been widely used, for the convenience of users, a free server called PseAAC (see ref”
PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. (2014, methodology): “1 Abbreviations used: PseAAC, pseudo amino acid composition; GPCR, G coupled receptor; PseKNC, pseudo K-tuple nucleotide composition; PseDNC dinucleotide composition; PseTNC, pseudo trinucleotide composition.”
iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences (2018, unknown): “, 2011), PseAAC (Shen and Chou, 2008), PseAAC-Builder (Du et al.”
Update of PROFEAT: a web server for computing structural and physicochemical features of proteins and peptides from amino acid sequence (2006, background): “From H1ði Þ and H2ði Þ defined in Equation (1) and (2), the hydrophobicity and hydrophilicity correlation functions are defined (32), respectively, as:”
iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. (2013, methodology): “Because it has been widely used, in 2012 a powerful soft-ware called PseAAC-Builder [56] was established for generating various special Chou’s pseudo-amino acid compositions, in addition to the web-server PseAAC [57] built in 2008.”
Impacts of bioinformatics to medicinal chemistry. (2015, methodology): “Because the approach of using PseAAC to represent protein/peptide sequences has been widely and increasingly used, in addition to the web-server “PseAAC” [114] built in 2008, recently three powerful open access soft-wares, called “PseAAC-Builder” [115], “propy” [116], and “PseAACGeneral” [117], were”
Pse-in-One: a web server for generating various modes of pseudo components of DNA, RNA, and protein sequences (2015, background): “Encouraged by the successes of using PseAAC to deal with protein / peptide sequences, the corresponding approaches were proposed recently to deal with DNA sequences (5–7) and RNA sequences (8).”
Some remarks on protein attribute prediction and pseudo amino acid composition (2010, methodology): “For the convenience of users, a web-server called ''PseAAC'' (Shen and Chou, 2008) was established at http://www.csbio.sjtu.edu.cn/bioinf/ PseAAC/, by which some commonly used PseAAC forms can be automatically generated.”