Prediction of protein subcellular localization

By Chin-Sheng Yu, Yu‐Ching Chen, Chih-Hao Lu, Jenn-Kang Hwang. 2006

1,670 citations recorded by the source corpus.

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In Molecular Biology

Citations in the corpus

Modeling aspects of the language of life through transfer-learning protein sequences (2019, methodology): “Subcellular localization prediction was trained and evaluated using the DeepLoc data set [47] for which performance was measured for several methods, namely: LocTree2 [72], MultiLoc2 [73], Sher-Loc2 [74], CELLO [75], iLoc-Euk [52], WoLF PSORT [76] and YLoc [77].”
KinasePhos 2.0: a web server for identifying protein kinase-specific phosphorylation sites based on sequences and coupling patterns (2007, methodology): “As to sequence surrounding catalytic sites, 9-mer sequences (� 4 �þ 4) of kinase-specific phosphorylation sites are encoded in three ways: BLOSUM62 profile encoding (the corresponding row number of amino acids in BLOSUM62 matrix), reduced alphabet (sparse encoding with fewer letters) ( 18 ) and 20-d”
PSORTb 3.0: improved protein subcellular localization prediction with refined localization subcategories and predictive capabilities for all prokaryotes (2010, background): “Availability: http://www.psort.org/psortb (download open source software or use the web interface).”
Modeling the language of life – Deep Learning Protein Sequences (2019, methodology): “Localization prediction was trained and evaluated using the DeepLoc data set [40] for which performance was measured for several methods, namely: LocTree2 [42], MultiLoc2 [43], SherLoc2 [44], CELLO [45], iLoc-Euk [46], WoLF PSORT [47] and YLoc [48].”
CELLO2GO: A Web Server for Protein subCELlular LOcalization Prediction with Functional Gene Ontology Annotation (2014, methodology): “By extending the hybrid approach [22], we report herein the implementation of the CELLO2GO server (http://cello.life.nctu.edu.tw/cello2go/), which provides brief and/or detailed annotations of GO terms related to homologs of a query protein found by BLAST searching in combination with a…”
Predicting linear B-cell epitopes using string kernels (2008, neutral): “, 2006) and protein subcellular localization prediction (Bulashevska and Eils, 2006; Yu et al., 2006).”
The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum (2013, methodology): “A parser for other predictors, such as CELLO is also included [36].”
Automated subcellular location determination and high-throughput microscopy. (2007, background): “Prediction approaches are typically based on the analysis of nucleic acid and amino acid sequences to seek sorting or signal peptides, sequence profiles, and similarities with known protein families (Chou and Shen, 2006; Lu et al., 2004; Yu et al., 2006).”
DeepLoc: prediction of protein subcellular localization using deep learning (2017, methodology): “The performance of our models were compared with a number of current prediction algorithms using the following approaches: LocTree2 (Goldberg et al., 2012), MultiLoc2 (Blum et al., 2009) and SherLoc2 (Briesemeister et al., 2009) were run with local command-line versions installed on our own server, ”