R-GAT: Cancer Document Classification Leveraging Graph-Based Residual Network for Scenarios with Limited Data

TL;DR

This paper introduces R-GAT, a lightweight graph-based neural network that effectively classifies cancer-related biomedical abstracts with limited data, offering a resource-efficient alternative to transformer models.

Contribution

The paper presents R-GAT, a novel residual graph attention network that captures semantic and relational dependencies in biomedical texts, performing competitively with less computational cost.

Findings

R-GAT achieves performance comparable to transformer models.

The model is robust under limited data conditions.

It requires significantly fewer computational resources.

Abstract

Accurate classification of cancer-related biomedical abstracts is critical for advancing cancer informatics and supporting decision-making in healthcare research. Yet progress in this domain is often constrained by limited availability of labeled corpora and the high computational demands of transformer-based approaches. To address these challenges, we propose a Residual Graph Attention Network (R-GAT) that integrates multi-head attention with residual connections to capture semantic and relational dependencies in biomedical texts. Evaluated on a curated dataset of 1,875 PubMed abstracts spanning thyroid, colon, lung, and generic cancer topics, R-GAT achieves stable and competitive performance, comparable to transformer-based models such as BioBERT and BioClinicalBERT and strong classical baselines like Logistic Regression, while requiring significantly fewer computational resources. Ablation studies confirm the importance of attention and residual connections in ensuring robustness under limited-data conditions. To support reproducibility and facilitate future research, we also release the curated dataset. Together, these contributions demonstrate the value of lightweight graph-based architectures as reliable and resource-efficient alternatives to computationally intensive transformers in biomedical NLP.