bioRxiv · 10.1101/2024.09.24.614785
LOCAS: Multi-label mRNA Localization with Supervised Contrastive Learning
Abstract
Traditional methods for mRNA subcellular localization often fail to account for multiple compartmentalization. Recent multi-label models have improved performance, but still face challenges in capturing complex localization patterns. We introduce LOCAS (Localization with Supervised Contrastive Learning), which integrates an RNA language model to generate initial embeddings, employs supervised contrastive learning (SCL) to identify distinct RNA clusters, and uses a multi-label classification head (ML-Decoder) with cross-attention for accurate predictions. Through extensive ablation studies and multi-label overlapping threshold tuning, LOCAS achieves state-of-the-art performance across all metrics, providing a robust solution for RNA localization tasks.
Explore related subjects
Keep this discovery
Explore connections, maps & timelines
Abir, A. R., Tahmid, M. T., Rahman, M. S.. 2024-09-26. LOCAS: Multi-label mRNA Localization with Supervised Contrastive Learning. https://doi.org/10.1101/2024.09.24.614785
Cite the original work for its findings. Save a collection to share your selection of sources.