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We employed 12 feature encoding schemes that cover nucleic acid composition, position-specific, and physicochemical properties information, whose corresponding optimal feature set identified and developed their respective baseline models using eXtreme Gradient Boosting (XGBoost) classifier. Subsequently, the predicted scores of 12 baseline models are integrated as a novel feature vector to train XGBoost and develop the final model. For a given sequence, Stack-ORI predicts its calss and probability values

Please select the appropriate cell-specific species for the prediction

Hompsapiens K562

Homosapiens MCF7

Homosapiens Hela

Mus musculus ES

Mus musculus MEF

Mus musculus P19

Drosophila Melanogaster Kc

Drosophila Melanogaster Bg3

Drosophila Melanogaster S2

Arabidopsis thaliana AT


Computational prediction and interpretation of cell-specific replication origin sites from multiple eukaryotes by exploiting stacking framework (Briefings in Bioinformatics).  
[Please cite this paper if you find Stack-ORI is useful in your research

Contact: weileyi@sdu.edu.cn