Cell-PLoc: a package of Web servers for predicting subcellular localization of proteins in various organisms

By Kuo-Chen Chou, Hongbin Shen. Nature Protocols, 2008

1,036 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0227bacf8ba169cbdd67dbe44060a63b58df6555

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Citations in the corpus

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].”
Impacts of bioinformatics to medicinal chemistry. (2015, methodology): “Proteins with multiple functions or dynamic feature of this kind are called multiplex proteins [130,131], which are particularly interesting because they are often the special targets for drug development [132].”
Cell-PLoc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms (2010, methodology): “predictors in the Cell-PLoc package [61], EukmPLoc and Hum- mPLoc can be also used to deal”
MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction (2009, methodology): “In addition to the predictors of the two categories, there are also hybrid approaches which combine sequence-based and annotation-based information [33-37] and can therefore profit from the advantages of both worlds.”
REVIEW : Recent advances in developing web-servers for predicting protein attributes (2009, background): “…B in [51] and were also at the same time placed 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…”
iLoc-Euk: A Multi-Label Classifier for Predicting the Subcellular Localization of Singleplex and Multiplex Eukaryotic Proteins (2011, background): “Accordingly, protein samples defined in a GO database space would be clustered in a way better reflecting their subcellular locations [26,52].”
iFC2: an integrated web-server for improved prediction of protein structural class, fold type, and secondary structure content (2010, methodology): “We chose this type of the test which randomizes the selection of the ten folds, instead of the jackknife crossvalidation which leads to a unique non-randomized result ( Chou and Shen 2007, 2008 ) and which was recently used in related works (Li et al. 2009; Lin et al. 2009; Nanni and Lumini 2009; Vi”
iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences (2018, background): “, 2007), subcellular locations (Chou and Shen, 2008), enzyme substrates (Barkan et al.”
Metabolic engineering toward enhanced LC-PUFA biosynthesis in Nannochloropsis oceanica: Overexpression of endogenous Δ12 desaturase driven by stress-inducible promoter leads to enhanced deposition of polyunsaturated fatty acids in TAG (2015, methodology): “dk/ services/TMHMM/) alongwith an ER subcellular-localization prediction (Plant-mPLoc; [65,66]).”
Using support vector machine combined with auto covariance to predict protein–protein interactions from protein sequences (2008, methodology): “However, for the statistical predictions, it is absolutely necessary to avoid redundancy and homology bias in the training data set ( 57 ).”
Some remarks on protein attribute prediction and pseudo amino acid composition (2010, methodology): “In general, the shortest protein sequence investigated is 50 amino acids (Chou and Shen, 2008a; Chou and Shen, 2010c), hence we can set the maximum value allowed for l is 49.”
BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions (2016, methodology): “Thirdly, the adjacency list file and the molecular features in the above two steps are provided to calculate the final interaction features tasks such as SVM-Prot [61], Cell-Ploc [36], iGPCR-Drug [64], iRSpot-PseDNC [39], IDrug-Target [65] and iNuc-PseKNC [66].”