# Semantic-enhanced heterogeneous graph learning for identifying ncRNAs associated with drug resistance

## TL;DR

This paper introduces a new method to identify non-coding RNAs linked to drug resistance using an enhanced graph learning approach that improves accuracy and interpretability.

## Contribution

The novel iNcRD-HG framework integrates heterogeneous molecular interactions and semantic context for better ncRNA-drug resistance predictions.

## Key findings

- iNcRD-HG outperforms existing methods on benchmark datasets for ncRNA-drug resistance association prediction.
- The framework captures complex contextual dependencies through relation-type-aware message passing.
- It provides interpretable insights into synergistic pathways involved in drug resistance.

## Abstract

Identifying non-coding RNAs (ncRNAs) associated with drug resistance is critical for elucidating molecular mechanisms underlying drug response, facilitating drug screening, and discovering novel therapeutic targets. While several graph neural network-based methods have been proposed to infer ncRNA-drug resistance associations, they remain fundamentally constrained by semantic distortion induced by a sparse bipartite network and neglect of relational semantics among molecular entities, ultimately compromising both predictive reliability and biological interpretability.

In this study, we propose iNcRD-HG, a novel framework for identifying ncRNA-drug resistance associations. The framework addresses three critical aspects: constructing a context-enriched heterogeneous network that integrates six distinct molecular interaction types with bio-entity-specific attributes, developing a semantic-enhanced graph learning architecture that implements relation-type-aware message passing to capture complex contextual dependencies, and introducing an interpretability mechanism to reveal potential synergistic pathways underlying drug response. Experimental results demonstrate that iNcRD-HG achieves superior predictive performance across diverse benchmark datasets while deriving association features with strong discriminative capability. By identifying molecular synergistic contexts, iNcRD-HG provides mechanistically interpretable insights into ncRNA-mediated drug resistance.

Datasets and source codes are available at https://github.com/Biohang/iNcRD-HG.