Predicting subcellular localization of proteins for Gram‐negative bacteria by support vector machines based on n‐peptide compositions

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Metabolic network analysis reveals microbial community interactions in anammox granules (2017, positive): “Organisms encoding the Cationic amino acid transporter (CAT) The subcellular location (extracellular, outer membrane or peroplasm) of each peptidase was predicted using the subcellular localization predictor (CELLO) 69 .”
CELLO2GO: A Web Server for Protein subCELlular LOcalization Prediction with Functional Gene Ontology Annotation (2014, methodology): “Among the programs that do not use a homology-based approach, CELLO [22,25] performs as well as one that requires a much larger amount of training data [26].”
Prediction of protein subcellular localization (2006, methodology): “Following the previous works, 16,28 we use the percentage accuracy to assess the accuracy of the subcellular localization identiﬁcation: Q i (cid:1) c i /n i , where c i is the number correctly predicted in the i th subcellular location, and n i is the number of sequences in that location.”