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Additional file 2. List of subcellular localization prediction methods
Name of the
predictor or author
PA-SUB
EpiLoc
Sequence feature
Sequence similarity and text annotation
ProLoc-GO
Gene Ontology term
PSLT
InterPro domains and specific membrane domains
Predotar
TargetP
iPSORT
Targeting peptide
Protein prowler
pSLIP
Physicochemical properties
Subloc
Computational methods
Naïve Bayes
Support Vector Machine
Genetic algorithm based method
combined with SVM
Likelihood calculated by Bayes'
rule
Neural network
Neural network
Alphabet indexing and pattern
rule
Neural network
Support Vector Machine
Support Vector Machine
Amino acid composition
NNPSL
LOCSVMPSI
ESLpred
Cai et al
LOCtree
Yuan
PSORTII
WoLF PSORT
Gao, et al
MITOPRED
pTARGET
MitoProt
SherLoc
MultiLoc
PSLDoc
KnowPred
YLoc
Neural network
Position-specific scoring matrix and amino acid
composition of four segments
Amino acid composition, 33 physicochemical properties,
dipeptide composition, PSI-blast result, and
combined feature of the above
Amino acid composition, quasi-sequence-order (up to 13
gaps), and physicochemical properties (hydrophobicity,
hydrophilicity, side-chain volume)
Evolutionary profiles, global amino acid composition,
50N-terminal amino acid composition, amino acid
composition in three secondary structure states,
and the output of signalP
Amino acid composition and paired amino acid
composition
A set of sequence-derived features
Features from iPSORT and PSORTII, together with amino
acid content
Amino acid composition, dipeptide composition, and
physicochemical properties
Pfam domains occurrence, amino acid composition, and PI
value
Pfam domains occurrence and amino acid composition
Targeting sequence and hydrophobicity characteristics
Amino acid composition, targeting signals, motif, and text
search
Amino acid composition, targeting signals, phylogenetic
profiles, motif, and GO term
Gapped dipeptide and position specific scoring matrix
Sequence similarity
Amino acid composition, normalized amino acid
composition, pseudo-amino acid composition, grouped
amino acid composition, PROSITE patterns and GO terms
Support Vector Machine
Support Vector Machine
Support Vector Machine
Support Vector Machine
Hidden Markov Model
K Nearest Neighbors
Weighted K Nearest Neighbors
K Nearest Neighbors
Score of different features
Score of different features
Multivariate analysis
Support Vector Machine
Support Vector Machine
Support Vector Machine
Similarity score
Naïve Bayes
* As new tools emerge each year, this list is not exhaustive and includes the representative tools
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