Euk-mPLoc: a fusion classifier for large-scale eukaryotic protein subcellular location prediction by incorporating multiple sites.

By Kuo-Chen Chou, Hongbin Shen. Journal of Proteome Research, 2007

310 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/956481f0eddff6679eaa992e50b543c3fc4317f0

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In Molecular Biology

Citations in the corpus

REVIEW : Recent advances in developing web-servers for predicting protein attributes (2009, background): “…in the downloadable file called Tab_Euk-mPLoc at http://chou.med.harvard.edu/bioinf/euk-multi/ [50] or http://202.120.37.186/bioinf/euk-multi/ [51], the predicted subcellular locations of the 334 eukaryotic proteins are given in column 4 of Appendix A , where for facilitating comparison the…”
Plant-mPLoc: A Top-Down Strategy to Augment the Power for Predicting Plant Protein Subcellular Localization (2010, background): “Since some proteins in S may occur in two or more locations, it is instructive to introduce the concept of ‘‘locative protein’’ [23], as briefed as follows.”
Pseudo Amino Acid Composition and its Applications in Bioinformatics, Proteomics and System Biology (2009, methodology): “The information extracted from the GO database [185187] was used to formulate PseAAC for predicting protein subcellular localization [50,76,79,122,124-128,130,175,188191], enzyme functional class [52,192], membrane protein types [193], and protein-protein interactions [160].”
A New Method for Predicting the Subcellular Localization of Eukaryotic Proteins with Both Single and Multiple Sites: Euk-mPLoc 2.0 (2010, unknown): “Listed in Table 2 are the results obtained with Euk-mPLoc [14] and Euk-mPLoc 2.”
YLoc—an interpretable web server for predicting subcellular localization (2010, methodology): “Note that YLoc+, WoLF PSORT and Euk-mPloc are evaluated using the generalized ACC and F 1 from multilabel classification (28).”
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.”
Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms (2008, methodology): “Programs The following predictors of the subcellular localization of proteins will be described in this protocol: Euk-mPLoc For predicting the subcellular localization of eukaryotic proteins including those with multiple location”
pSuc-Lys: Predict lysine succinylation sites in proteins with PseAAC and ensemble random forest approach. (2016, methodology): “First, its outcome is finalized by fusing the results yielded from an array of individual predictors via a voting system, and hence more stable and reliable as proved in a series of previous studies [32; 65-67; 94100].”
iLoc-Euk: A Multi-Label Classifier for Predicting the Subcellular Localization of Singleplex and Multiplex Eukaryotic Proteins (2011, unknown): “The 22 locations are: (1) acrosome, (2) cell membrane, (3) cell wall, (4) centriole, (5) chloroplast, (6) cyanelle, (7) cytoplasm, (8) cytoskeleton, (9) endoplasmic reticulum, (10) endosome, (11) extracellular, (12) Golgi apparatus, (13) hydrogenosome, (14) lysosome, (15) melanosome, (16) microsome ”
Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms (2010, methodology): “ries of previous publications [62-67,108,109], and hence”
MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction (2009, background): “A further challenge is how to deal with proteins present in multiple locations [40,41].”
Recent progress in protein subcellular location prediction. (2007, background): “One is for the human protein system [39], and the other is for the eukaryotic protein system [42].”
Multitask Learning for Protein Subcellular Location Prediction (2011, background): “We find that if the organisms are very different or are remotely related from a biological point of view, then jointly training the multiple models cannot lead to significant improvement.”