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Fig. 1 | BMC Bioinformatics

Fig. 1

From: G4Boost: a machine learning-based tool for quadruplex identification and stability prediction

Fig. 1

Summary of the G4Boost workflow. G4 motifs were extracted from several plant genomes and evaluated thermodynamically for folding into secondary structures to construct the training data (Phase 1). G4 motifs were described by ten features and labeled by their folding probability and energy (Phase 1). Classification models were evaluated for their prediction accuracy of the G4 structure folding (Phase 2). Regression models were evaluated and optimized for the prediction of G4 structure folding energy (Phase 2). Final prediction machinery, G4Boost, is built for the prediction of G4 structure topology, folding probability, and folding energy (Phase 3) and evaluated (Evaluation)

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