MoXGATE: Modality-aware cross-attention for multi-omic gastrointestinal cancer sub-type classification

TL;DR

MoXGATE is a deep-learning framework that uses modality-aware cross-attention to effectively integrate multi-omic data for accurate gastrointestinal cancer subtype classification, demonstrating superior performance and interpretability.

Contribution

The paper introduces a novel cross-attention-based multi-omic integration framework with modality-weighted fusion and validation across multiple cancer types.

Findings

Achieves 95% classification accuracy on GIAC and BRCA datasets.

Outperforms existing methods in multi-omic cancer subtype classification.

Model generalizes well to unseen cancer types, e.g., breast cancer.

Abstract

Cancer subtype classification is crucial for personalized treatment and prognostic assessment. However, effectively integrating multi-omic data remains challenging due to the heterogeneous nature of genomic, epigenomic, and transcriptomic features. In this work, we propose Modality-Aware Cross-Attention MoXGATE, a novel deep-learning framework that leverages cross-attention and learnable modality weights to enhance feature fusion across multiple omics sources. Our approach effectively captures inter-modality dependencies, ensuring robust and interpretable integration. Through experiments on Gastrointestinal Adenocarcinoma (GIAC) and Breast Cancer (BRCA) datasets from TCGA, we demonstrate that MoXGATE outperforms existing methods, achieving 95\% classification accuracy. Ablation studies validate the effectiveness of cross-attention over simple concatenation and highlight the importance of different omics modalities. Moreover, our model generalizes well to unseen cancer types e.g., breast cancer, underscoring its adaptability. Key contributions include (1) a cross-attention-based multi-omic integration framework, (2) modality-weighted fusion for enhanced interpretability, (3) application of focal loss to mitigate data imbalance, and (4) validation across multiple cancer subtypes. Our results indicate that MoXGATE is a promising approach for multi-omic cancer subtype classification, offering improved performance and biological generalizability.