Table 2.
The previous researches in the prediction of protein subcellular localization
| Author(s) | Algorithm | Feature | # of Classes | Multi-label | Imbalance |
|---|---|---|---|---|---|
| Nakashima and Nishikawa (4) | Scoring System | AAk, PairAAl | 2 | x | x |
| Cedano et al. (13) | LDa (Mahalanobis) | AAk | 5 | x | x |
| Reinhardt and Hubbard (20) | ANNc Approach | AAk | 3, 4 | x | x |
| Chou and Elrod (2) | CDd | AAk | 12 | x | x |
| Yuan (21) | Markov Model | AAk | 3, 4 | x | x |
| Nakai and Horton (23) | k-NNe Approach | Signal Motif | 11 | x | x |
| Emanuelsson et al. (10) | Neural network | Signal Motif | 4 | x | x |
| Drawid et al. (27) | CDd | Gene Expression Pattern | 8 | x | x |
| Drawid and Gerstein (24) | BNb Approach | Signal Motif, HDEL motif | 5, 6 | x | x |
| Cai et al. (8) | SVMsi | AAk | 12 | x | x |
| Chou (6) | Augumented CDd | AAk, SOCn factor | 5, 7, 12 | x | x |
| Hua and Sun (16) | SVMsi | AAk | 4 | x | x |
| Chou and Cai (25) | SVMsi | SBASE-FunDo | 12 | x | x |
| Nair and Rost (26) | NNe Approach | functional annotation | 10 | x | x |
| Cai et al. (9) | SVMsi | SBASE-FunDo, PseAAm | 5 | x | x |
| Chou and Cai (11) | NNe Approach | GOp, InterPro-FunDq, PseAAm | 3, 4 | x | x |
| Chou and Cai (14) | LDa | PseAAm | 14 | x | x |
| Pan et al. (19) | Augmented CDd | PseAAm with filler | 12 | x | x |
| Park and Kanehisa (18) | SVMsi | AAk, PairAAm, GapAAk | 12 | x | x |
| Zhou and Doctor (22) | CDd | AAk | 4 | x | x |
| Gardy et al. (12) | SVMsi, HMMh, BNb | AAk, motif, homology analysis | 5 | x | x |
| Huang and Li (17) | fuzzy k-NNe | PairAAl | 4, 11 | x | x |
| Guo et al. (15) | p-ANNj | AAk | 8 | x | x |
| Bhasin and Raghava (7) | SVMsi | AAk, PairAAl | 4 | x | x |
| Chou and Cai (1) | NNe Approach | GOp, InterPro-FunDq, PseAAm | 22 | Considering | x |
aLD: Least Distance algorithm.
bBN: Bayesian Network.
cANN: Artificial Neural Network.
dCD: Covariant Discriminant algorithm.
eNN: Nearest Neighbor.
hHMM: Hidden Markov Model.
iSVMs: Support Vector Machines.
jp-ANN: probabilistic Artificial Neural Network.
kAA: amino acid composition.
lPairAA: amino acid pair composition.
mPseAA: pseudo amino acid composition.
nSOC: sequence-order correlation.
oSBASE-FunD: functional domain composition using SBASE.
pGO: gene ontology.
qInterPro-FunD: InterPro functional domain composition.
rFunDC: functional domain composition. (Here, ‘x’ means ‘Not Considering’.)