SecretP: Identifying bacterial secreted proteins by fusing new features into Chou’s pseudo-amino acid composition

Predicting Subchloroplast Locations of Proteins Based on the General Form of Chou's Pseudo Amino Acid Composition: Approached From Optimal Tripeptide Composition

“…For a summary about its recent development and applications, see a comprehensive review [8]. Ever since the concept of PseAAC was proposed by Chou [6] in 2001, it has rapidly penetrated into almost all the fields of protein attribute prediction, such as predicting protein structural classes [37,56], predicting protein quaternary structure [76], identifying bacterial virulent proteins [52], identifying cell wall lytic enzymes [18], identifying risk type of human papillomaviruses [22], identifying DNA-binding proteins [24], predicting homo-oligomeric proteins [55], predicting protein secondary structure content [3], predicting supersecondary structure [83], predicting enzyme family and sub-family classes [54,66,82], predicting protein subcellular location [35,36,80], predicting subcellular localization of apoptosis proteins [32,35,44,19], predicting protein subnuclear location [33], predicting protein submitochondria locations [75,51], predicting G-Protein-Coupled Receptor Classes [27,53], predicting protein folding rates [28], predicting outer membrane proteins [39], predicting cyclin proteins [48], predicting GABA(A) receptor proteins [49], identifying bacterial secreted proteins [73], identifying the cofactors of oxidoreductases [77], identifying lipase types [78], identifying protease family [30], predicting Golgi protein types [17], classifying amino acids [26], among many ot...…”

Predicting Viral Protein Subcellular Localization with Chou's Pseudo Amino Acid Composition and Imbalance-Weighted Multi-Label K-Nearest Neighbor Algorithm

“…Ever since the concept of PseAAC was proposes by Chou [24] in 2001, its applications have been rapidly spread into almost all the fields of protein attribute prediction [28], such as predicting protein secondary structure content [29], predicting supersecondary structure [30], predicting protein structural classes [31,32], predicting protein quaternary structure [33], predicting enzyme family and sub-family classes [34][35][36], predicting protein subcellular location [37,38], predicting subcellular localization of apoptosis proteins [5,[39][40][41], predicting protein subnuclear location [42], predicting protein submitochondria locations [43][44][45], identifying cell wall lytic enzymes [40], identifying risk type of human papillomaviruses [46], identifying DNA-binding proteins [47], predicting G-Protein-Coupled Receptor Classes [48,49], predicting protein folding rates [50], predicting outer membrane proteins [51], predicting cyclin proteins [52], predicting GABA(A) receptor proteins [53], identifying bacterial secreted proteins [54], identifying the cofactors of oxidoreductases [55], identifying lipase types [56], identifying protease family [57], predicting Golgi protein types [58], classifying amino acids [59], among many others. For a brief introduction about Chou's PseAAC, visit the Wikipedia web-page at http://en.wikipedia.org/wiki/Pseudo_amino_acid_compositio n/.…”

Predicting protein submitochondria locations by combining different descriptors into the general form of Chou’s pseudo amino acid composition

“…For a brief introduction to Chou's PseAAC, visit the Wikipedia Web page at http://en.wikipedia.org/wiki/ Pseudo_amino_acid_composition. Ever since the concept of PseAAC was introduced, it has been widely used to study various problems in proteins and protein-related systems [see, e.g., (Chen et al 2009;Ding et al 2009;Esmaeili et al 2010;Georgiou et al 2009;Gu et al 2010a;Jiang et al 2008a, b;Li and Li 2008a;Lin 2008;Lin et al 2008;Mohabatkar 2010;Mohabatkar et al 2011;Qiu et al 2010;Yu et al 2010;Zeng et al 2009;Zhou et al 2007)]. For various different modes of PseAAC, see Chou (2009).…”