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L2S-MirLoc: A Lightweight Two Stage MiRNA Sub-Cellular Localization Prediction Framework

Muhammad Nabeel Asim; Muhammad Ali Ibrahim; Christoph Zehe; Olivier Cloarec; Rickard Sjogren; Johan Trygg; Andreas Dengel; Sheraz Ahmed
In: IJCNN 2021 Conference Proceedings. International Joint Conference on Neural Networks (IJCNN-2021), July 18-22, ISBN 978-0-7381-3366-9, IEEE, 2021.


A comprehensive understanding of miRNA sub-cellular localization may leads towards better understanding of physiological processes and support the fixation of diverse irregularities present in a variety of organisms. To date, diverse computational methodologies have been proposed to automatically infer sub-cellular localization of miR-NAs solely using sequence information, however, existing approaches lack in performance. Considering the success of data transformation approaches in Natural Language Processing which primarily transform multi-label classification problem into multi-class classification problem, here, we introduce three different data transformation approaches namely binary relevance, label power set, and classifier chains. Using data transformation approaches, at 1 st stage, multi-label miRNA sub-cellular localization problem is transformed into multi-class problem. Then, at 2 nd stage, 3 different machine learning classifiers are used to estimate which classifier performs better with what data transformation approach for hand on task. Empirical evaluation on independent test set indicates that L2S-MirLoc selected combination based on binary relevance and deep random forest outperforms state-of-the-art performance values by significant margin.