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Welcome to CircRNA Sub-Cellular Localization Prediction!

Circ-LocNet: A very first Circular RNA Sub-Cellular Localization Prediction Framework allows the user to explore the performance of 7 different residue frequency based, residue order and frequency based, and physio-chemical property based sequence descriptors using 5 most widely used machine learning classifiers. It also enables the user to perform K-order sequence descriptor fusion where user can combine similar as well as dismilar genre of sequence descriptors in combination of 2,3,4, and going all way up to combining all 7 sequence descriptors in order to optimize statistical representation of CircRNA sequences. Circ-LocNet can be used to analyze sub-cellular localization distribution of CircRNAs of diverse species as well as to validate experimentally identifed sub-cellular localization. CircLoc-Net facilitates benchmark Circular RNA Sub-Cellular Localization Prediction dataset, train and optimize feature extraction as well classification strategies, perform inference on novel CircRNA sequences, and download interactive artifacts during the lifetime of session.