MMnc: multi-modal interpretable representation for non-coding RNA classification and class annotation.

ABSTRACT:

Motivation

As the biological roles and disease implications of non-coding RNAs continue to emerge, the need to thoroughly characterize previously unexplored non-coding RNAs becomes increasingly urgent. These molecules hold potential as biomarkers and therapeutic targets. However, the vast and complex nature of non-coding RNAs data presents a challenge. We introduce MMnc, an interpretable deep-learning approach designed to classify non-coding RNAs into functional groups. MMnc leverages multiple data sources-such as the sequence, secondary structure, and expression-using attention-based multi-modal data integration. This ensures the learning of meaningful representations while accounting for missing sources in some samples.

Results

Our findings demonstrate that MMnc achieves h

SUBMITTER: Creux C 

PROVIDER: S-EPMC11890286 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature