Using Chou's amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes.

By Xibin Zhou, Chao Chen, Zhanchao Li, Xiaoyong Zou. Journal of Theoretical Biology, 2007

302 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/6a84128792a72c5e82d1ce89cb2540f1bb618cfc

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. Hongbin Shen
3. Hao Lin
4. Xin Gao
5. Ling-Yun Wu
6. Jun Ding
7. Yan Xu
8. Pengmian Feng

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): “, [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]).”
• mlDEEPre: Multi-Functional Enzyme Function Prediction With Hierarchical Multi-Label Deep Learning (2019, methodology): “We compared our method with ML-KNN (Zhang and Zhou, 2007), BR-KNN (Spyromitros et al., 2008), IBLR-ML (Cheng and Hüllermeier, 2009), GM (Zou and Xiao, 2016), and SVM-NN (Amidi et al., 2017).”
• DEEPre: sequence-based enzyme EC number prediction by deep learning (2017, unknown): “…Huang et al. , 2007; Kumar and Choudhary, 2012; Lee et al. , 2008; Li et al. , 2016; Lu et al. , 2007; Nasibov and Kandemir-Cavas, 2009; Qiu et al. , 2009, 2010; Sharif et al. , 2015; Shen and Chou, 2007; Volpato et al. , 2013; Wang et al. , 2010, 2011; Zhou et al. , 2007; Zou and Xiao, 2016).”
• iRSpot-PseDNC: identify recombination spots with pseudo dinucleotide composition (2013, background): “Since then, the concept of PseAAC has penetrated into almost all the fields of computational proteomics, such as predicting protein submitochondrial localization (8), predicting protein structural class (9), predicting DNA-binding proteins (10), identifying bacterial virulent proteins (11), predicti”
• Multitask Learning for Protein Subcellular Location Prediction (2011, unknown): “, [19], [20], [24], [26], [56], [57], [58], [59], [60], [61], [62],”
• REVIEW : Recent advances in developing web-servers for predicting protein attributes (2009, background): “During the last six years, a number of predictors have been developed to address these problems [53,120-125].”
• 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”
• Some remarks on protein attribute prediction and pseudo amino acid composition (2010, methodology): “The concept of PseAAC has been widely used to study various problems in proteins and protein-related systems, such as predicting enzymes and their family/sub-family classification (Cai and Chou, 2005; Cai et al., 2005; Qiu et al., 2010; Wang et al., 2010b; Zhou et al., 2007), protein subcellular loc”
• PseKNC: a flexible web server for generating pseudo K-tuple nucleotide composition. (2014, background): “families [5], identifying submitochondrial localization of proteins [6], detecting remote homologous proteins [7], predicting G-protein-coupled receptor (GPCR) classes [8], predicting protein structural classes [9], identifying risk type of human papillomaviruses [10], predicting GABAA receptor prot”