Subcellular location prediction of apoptosis proteins

By Guo-Ping Zhou, Kutbuddin S. Doctor. Proteins: Structure, Function, and Bioinformatics, 2002

356 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3b03f2704684e9bcbd31d840dc04a65e48a94fcd

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In Molecular Biology

1. Kuo-Chen Chou
2. Qiang Yang
3. Hongbin Shen
4. Xuan Xiao
5. Qian Xu
6. Zhi-cheng Wu
7. Hong-Bin Shen
8. Sinno Jialin Pan

Citations in the corpus

• iAMP-2L: a two-level multi-label classifier for identifying antimicrobial peptides and their functional types. (2013, neutral): “, [22–25]).”
• Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites. (2007, neutral): “The predictor obtained through the above modified procedures is called Euk-mPLoc (i) that Figure 1. Schematic illustration to show the 22 subcellular locations of eukaryotic proteins: (1) acrosome, (2) cell wall, (3) centriole, (4) chloroplast, (5) cyanelle, (6) cytoplasm, (7) cytoskeleton, (8) endo”
• REVIEW : Recent advances in developing web-servers for predicting protein attributes (2009, background): “, [6-48] and the relevant references cited in a recent review paper [49].”
• Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms (2008, methodology): “…the jackknife test is deemed the most objective 89 , and has been increasingly used by investigators to examine the accuracy of various predictors 24,25,28,33,36,39,42,45,50–56, However, even being tested by the jackknife cross-validation, the same predictor can still yield different success…”
• Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms (2010, neutral): “, [4-59].”
• Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization (2010, background): “, [3,4,5,6,7,8,9,10] as well as a long list of references cited in two comprehensive review articles [11,12]).”
• Ensemble classifier for protein fold pattern recognition (2006, background): “Of the above six features, only the amino acid composition contains 20 components, with each representing the occurrence frequency of one of the 20 native amino acids in a given protein (Chou & Zhang, 1994; Zhou & Doctor, 2003).”
• Multitask Learning for Protein Subcellular Location Prediction (2011, methodology): “We adapt and compare two specializations of the multitask learning algorithms on 20 different organisms.”
• Some remarks on protein attribute prediction and pseudo amino acid composition (2010, methodology): “Many methods for predicting various protein attributes were based on the AAC-discrete model (see, e.g., Cedano et al., 1997; Chou, 1999 Chou, , 2000 Chou, , 2005b Zhang, 1992, 1995; Elrod, 1999, 2002; Chou, 1989; Feng et al., 2005; Jahandideh et al., 2007a; Klein, 1986; Klein and Delisi, 1986; Liu a”
• MemType-2L: a web server for predicting membrane proteins and their types by incorporating evolution information through Pse-PSSM. (2007, methodology): “Among the three cross-validation examinations, the jackknife test is deemed the most rigorous and objective [40], and hence has been increasingly adopted by investigators [7–9,11–13,19,24,26–29,31–35,37,41–53] in examining the quality of various prediction methods.”
• Using amphiphilic pseudo amino acid composition to predict enzyme subfamily classes (2005, background): “1999; Hua & Sun, 2001; Nakai, 2000; Nakai & Kanehisa, 1991; Nakashima & Nishikawa, 1994; Zhou & Doctor, 2003 ).”
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