ESLpred: SVM-based method for subcellular localization of eukaryotic proteins using dipeptide composition and PSI-BLAST

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Discriminatively trained Markov model for sequence classification (2005, methodology): “[4] and Hua et.”
Classification of Nuclear Receptors Based on Amino Acid Composition and Dipeptide Composition* (2004, unknown): “In the past, dipeptide composition has been used for the prediction of subcellular localization of proteins (11) and for fold recognition (12,13).”
MultiLoc2: integrating phylogeny and Gene Ontology terms improves subcellular protein localization prediction (2009, background): “In addition to the predictors of the two categories, there are also hybrid approaches which combine sequencebased and annotation-based information [33-37] and can therefore profit from the advantages of both worlds.”
Pseudo Amino Acid Composition and its Applications in Bioinformatics, Proteomics and System Biology (2009, methodology): “Lin and Li [109] used the dipeptide components [172,173] as the PseAAC components for predicting protein structural class.”
BaCelLo: a balanced subcellular localization predictor (2006, methodology): “Some of them discriminate among three classes, namely TARGETp (Emanuelsson et al ., 2000), ProteinProwler (Boden and Hawkins, 2005), SLP-local (Matsuda et al ., 2005) and Predotar (Small et al ., 2004); others discriminate among four classes in animals and ﬁve classes in plants and are Loctree, SubL”