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In this paper, we propose a method based on logic minimization to extract predictive rules for two bioinformatics problems involving the identification of functional sites in molecular sequences: transcription factor binding sites (TFBS) in DNA and O-glycosylation sines in proteins.
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All of these algorithms require the application of a complex procedure to the trained SVM or ANN to extract the rules, while in the case of the logic minimization method proposed in the present study, the creation of the classification system and the discovery of the rules describing the dataset are the same, without the need to implement additional steps (see Figure 1).
Therefore, beyond the static view of RNA folding using energy minimization methods to predict the final state of the folding, a time-dependent view is desired in order to extract information on the folding kinetics.
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This simplifies the nonlinear minimization to (6).
The obtained models were submitted to 100 cycles of energy minimization to regularize the geometry.
After each step, we executed a minimization to fit the new residues to the old structure.
Instead, we employ a robust L1-norm minimization to obtain A L 1.
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