Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes

By Kuo-Chen Chou. Bioinform., 2005

944 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/ae6267ba88a05de83ea4b6c4a912d5fdf1782d92

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. Bin Liu
4. Xuan Xiao
5. Jianhua Jia
6. Hao Lin
7. Xin Gao
8. Zi Liu

Citations in the corpus

• DEEPre: sequence-based enzyme EC number prediction by deep learning (2017, methodology): “…the enzyme using machine learning algorithms is the most extensively studied direction (Cai et al. , 2003, 2004, 2005; Cai and Chou, 2005; Chou, 2005; Chou and Elrod, 2003; De Ferrari et al. , 2012; Huang et al. , 2007; Kumar and Choudhary, 2012; Lee et al. , 2008; Li et al. , 2016; Lu…”
• iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. (2013, methodology): “Since in the current study there are five physical-chemical properties to be taken into consideration, similar to the dimension-augmenting approach in [32], we should use a (20 + 5k) - D feature vector to represent a sample; i.”
• pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. (2016, methodology): “The present study was devoted to developing a more powerful predictor by the pseudo amino acid composition or PseAAC via incorporating a vectorized sequence-coupling model [10] into the general form of pseudo amino acid composition (PseAAC) [11; 12] and ensemble random forest approach [13; 14].”
• Recent progress in protein subcellular location prediction. (2007, methodology): “The others, such as the physicochemical distance mode [59] and the amphiphilic pattern mode [60], can also be used to derive different types of PseAA composition.”
• iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences (2018, background): “This group of descriptors has been proposed in (Chou, 2001; Chou, 2005).”
• Ensemble classifier for protein fold pattern recognition (2006, methodology): “However, rather than using a combined correlation function (Chou, 2001), here the alternate correlation function between hydrophobicity and hydrophilicity (Chou, 2005; Chou & Cai, 2005) is adopted to reflect the sequenceorder effects.”
• MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. (2007, methodology): “To avoid complete loss of the sequence-order information, the concept of the pseudo amino acid composition as originally proposed in [25,38] was adopted; i.”
• A New Method for Predicting the Subcellular Localization of Eukaryotic Proteins with Both Single and Multiple Sites: Euk-mPLoc 2.0 (2010, positive): “The Euk-mPLoc predictor was established by hybridizing the ‘‘higher-level’’ GO (gene ontology [15]) approach and PseAAC (pseudo amino acid composition [16,17]) approach.”
• Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms (2010, methodology): “(3) Although the PseAAC approach was used as a complement in Cell-PLoc [61] that could take some partial sequence order effects into account, the original PseAAC [16,69] did not contain the sequential evolution and functional domain information, and hence would affect the prediction quality.”