Review Article
A Survey of Computational Intelligence Techniques in Protein Function Prediction
Table 9
Summary of computational intelligence for protein function prediction by using gene expression data.
| Reference | CI techniques | Performance | Datasets |
| [128] | Multilayer perceptron | TP rate: up to 79.6%, FP rate: up to 97% | DNA array expression data | [129] | MRF with Bayesian | Sensitivity: 87% | PPI, genetic interactions, highly correlated gene expression network, protein complex data, and structural properties | [130] | SVM | Accuracy: 89.44 | Gene expression data | [131] | Genetic programming | Accuracy: 92.50–98.7% | Gene expression data | [132] | Majority voting genetic programming | Accuracy: 81.82% | Gene expression data | [133] | Genetic programming | Accuracy: 94.9–99.27% | Gene expression data | [134] | Genetic programming | Accuracy: 95.24–100% | Gene expression values and constant values | [135] | Fuzzy nearest cluster | Top N accuracy: 65.27% | Gene expression data | [136] | -means | Accuracy: 0.16–0.24 | PPI and gene expression data | [137] | Hypergraph | Accuracy: 97.95% | Gene expression data | [138] | Discriminative local subspaces with SVM | Average precision: 63% and score: 0.44 | Gene expression data |
|
|