Locating proteins in the cell using TargetP, SignalP and related tools

By Olof Emanuelsson, Søren Brunak, G. von Heijne, Henrik Nielsen. Nature Protocols, 2007

1,142 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/af2be855bb1871cb70f58547b1b4b1a49661a188

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Citations in the corpus

DeepLoc: prediction of protein subcellular localization using deep learning (2017, background): “Therefore, predicting the subcellular localization of the proteins is an essential task which has been extensively studied in bioinformatics (Emanuelsson et al., 2007; Imai and Nakai, 2010; Wan and Mak, 2015).”
Probabilistic variable-length segmentation of protein sequences for discriminative motif discovery (DiMotif) and sequence embedding (ProtVecX) (2018, methodology): “The 4 classes in this dataset are (i) 371 mitochondrial proteins, (ii) 715 pathway or signal peptides, (iii) 1214 nuclear proteins, and (iv) 438 cytosolic protein sequences [45], where the redundant proteins are removed.”
An introduction to deep learning on biological sequence data: examples and solutions (2017, background): “The task of predicting protein sorting or subcellular localization has attracted large interest in the bioinformatics field (Emanuelsson et al. , 2007) and can be generally stated as a sequence to multi-class prediction problem.”
Variation Interpretation Predictors: Principles, Types, Performance, and Choice (2016, methodology): “Using WoLF PSORT [Horton et al., 2007] and a pipeline of several tools [Emanuelsson et al., 2007], disease-causing variations were studied in relation to protein localization [Laurila and Vihinen, 2009].”
The Transporter Classification Database (TCDB): recent advances (2015, background): “Extensive experimental work has established that phylogenetic data can also be used to predict substrate specificity, polarity of transport and even intracellular localization, depending on the family and degree of sequence divergence observed (1, 4 ).”
Recent progress in protein subcellular location prediction. (2007, methodology): “When some of the query proteins do not have the corresponding GO numbers, such as synthesized or hypothetical protein sequences [6], their subcellular localization will be identified automatically by the state-of-the-art prediction engine based on the PseAA composition model, and the success rates o”