MFF-LncLoc: Subcellular Localization Prediction of lncRNAs Based on Multi-Feature Fusion Using Transformers

The subcellular localization of long non-coding RNAs (lncRNAs) is fundamental to understanding their functional roles in gene regulation and disease mechanisms. Existing prediction models typically rely on single-source features, which often fail to capture the full complexity of lncRNA localization. To address this limitation, we propose MFF-LncLoc, a Transformer-based model designed to integrate multiple feature types for improved prediction accuracy. MFF-LncLoc processes embedding matrices derived from a non-overlapping trinucleotide approach, leveraging the Transformer’s capacity to capture both contextual and positional information to extract global features. Unlike conventional models that primarily focus on k-mer frequency features, MFF-LncLoc incorporates a diverse set of features, including statistical properties, sequence characteristics, and secondary structure information. These multi-dimensional features are then processed by a Convolutional Neural Network (CNN) to extract local sequence patterns, followed by a fully connected layer for subcellular localization prediction. Ablation studies confirm that the inclusion of multi-feature data significantly enhances model performance. MFF-LncLoc outperforms existing models across several metrics, including accuracy (ACC), macro-recall, macro-F1, AUC, and AUPR, demonstrating that the integration of diverse features offers substantial improvements over traditional single-feature approaches. The source code and dataset are available at https://github.com/ZiyuFanCSU/MFFLncLoc.