LOCAS: Multi-label mRNA Localization with Supervised Contrastive Learning
Abir, A. R.; Tahmid, M. T.; Rahman, M. S.
Show 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.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- sincFold: end-to-end learning of short- and long-range interactions in RNA secondary structure 95%
- Species-Agnostic Transfer Learning for Cross-species Transcriptomics Data Integration without Gene Orthology 94%
- Graph Contrastive Learning as a Versatile Foundation for Advanced scRNA-seq Data Analysis 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.