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Figure 1 | BMC Bioinformatics

Figure 1

From: Genetic algorithm learning as a robust approach to RNA editing site prediction

Figure 1

GA optimized weights for six variables in REGAL. We selected six variables and utilized the GA to optimize the weights for these variables. The greater the importance of a variable, the higher the value as shown here. Variables were abbreviated as follows: 1 = codon_transition, transition probability for codon pre- and post-edit; 2 = +1_nucleotide, nucleotide in +1 position relative to candidate C; 3 = hydrophobicity, likelihood that edit will yield a more hydrophobic amino acid than the unedited codon; 4 = amino_acid_transition, transition probability for amino acid pre- and post-edit; 5 = codon_position, position of the candidate edit site with respect to the codon (i.e. first, second or third position); and 6 = -1_nucleotide, nucleotide in the -1 position relative to candidate C.

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