Review Article
Molecular Mechanisms and Function Prediction of Long Noncoding RNA
Table 1
Machine-learning methods for identifying lncRNAs.
| Method | Features | Algorithm | References |
| | Peptide length | | | | Amino acid composition | | | | Hydrophobicity | | | CONC | Secondary structure content | SVM | [18] | | Percentage of residues exposed to solvent | | | | Sequence compositional entropy | | | | Number of homologs obtained by PSI-BLAST | | | | Alignment entropy | | |
| | ORF prediction quality | | | CPC | Number of homologs obtained by BLASTX | SVM | [19, 20] | | Alignment quality | | | | Segment distribution | | |
| Lu et al. | RNA-seq experiments Tilling arrays poly-A + RNA-seq experiments poly-A + tilling arrays GC content DNA conservation Predicted protein sequence conservation Predicted secondary structure free energy Predicted secondary structure conservation | Naïve Bayes Bayes Net Decision Tree Random Forest Logistic Regression SVM | [21] |
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