MTTSCL: protein subcellular localization prediction using multi-task learning

Protein subcellular localization prediction is an important research area in bioinformatics. Traditionally, machine learning techniques have been utilized to model specific cell types for this task. However, the majority of these approaches solely focus on modeling a specific cell type. It is believed that there are potential similarities and shared semantic information among different biological types that can be leveraged for improved predictions. To address this issue, a deep learning approach based on multi-task learning for protein subcellular localization (Multi-Task Transferable Shared Concept Learning, MTTSCL) is proposed. This approach aims to overcome the limitations imposed by the scale of labeled data and effectively transfer knowledge between different cell types to predict protein subcellular localization. The results demonstrated that the multi-task learning approach outperformed single-task modeling. The joint modeling of diverse biological classifications led to substantial enhancements in the accuracy of protein subcellular localization prediction. In summary, the proposed deep learning approach using multi-task learning provides a promising solution for protein subcellular localization prediction.