E-MuLA: An Ensemble Multi-Localized Attention Feature Extraction Network for Viral Protein Subcellular Localization
Grace-Mercure Bakanina Kissanga,
Hasan Zulfiqar,
Shenghan Gao,
Sophyani Banaamwini Yussif,
Biffon Manyura Momanyi,
Lin Ning,
Hao Lin,
Cheng-Bing Huang
Affiliations
Grace-Mercure Bakanina Kissanga
School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
Hasan Zulfiqar
Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, China
Shenghan Gao
School of Computer Science, Georgia Institute of Technology, Atlanta, GA 30332, USA
Sophyani Banaamwini Yussif
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
Biffon Manyura Momanyi
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610056, China
Lin Ning
School of Healthcare Technology, Chengdu Neusoft University, Chengdu 611844, China
Hao Lin
School of Life Science and Technology and Center for Informational Biology, University of Electronic Science and Technology of China, Chengdu 610054, China
Cheng-Bing Huang
School of Computer Science and Technology, Aba Teachers University, Aba 623002, China
Accurate prediction of subcellular localization of viral proteins is crucial for understanding their functions and developing effective antiviral drugs. However, this task poses a significant challenge, especially when relying on expensive and time-consuming classical biological experiments. In this study, we introduced a computational model called E-MuLA, based on a deep learning network that combines multiple local attention modules to enhance feature extraction from protein sequences. The superior performance of the E-MuLA has been demonstrated through extensive comparisons with LSTM, CNN, AdaBoost, decision trees, KNN, and other state-of-the-art methods. It is noteworthy that the E-MuLA achieved an accuracy of 94.87%, specificity of 98.81%, and sensitivity of 84.18%, indicating that E-MuLA has the potential to become an effective tool for predicting virus subcellular localization.
