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.

L2S-MirLoc_A_Lightweight_Two_Stage_MiRNA_Sub-Cellular_Localization_Prediction_Framework.pdf (pdf, 3 MB )

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz