In our recent research study, we systematically evaluated a range of neural network architectures to address the intricate challenge of integrating and interpreting multi-omics data within oncological research. This effort was motivated by the need to better understand the complex biological interactions underlying cancer, which cannot be fully captured by traditional analytical approaches. Cancer research often grapples with the challenge of integrating diverse types of omics data—genomic, transcriptomic, proteomic, and metabolomic—to form a cohesive understanding of tumor biology.
Traditionally, multi-omics data interpretation involves collecting and preprocessing data across these layers—such as sequencing genomic DNA, profiling mRNA transcripts, analyzing protein expressions, and quantifying metabolites. However, conventional methods often struggle with the complexity and volume of these data types, particularly when dealing with the high-dimensional and interconnected nature of cancerous systems.
Our methodology aimed to overcome these limitations by leveraging advanced neural network architectures. We conducted a detailed comparative analysis of Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transformer Networks, and Graph Neural Networks (GNNs) through several experimental phases.
GNNs demonstrated superior performance. We implemented advanced GNN architectures, including Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and Graph Isomorphism Networks (GINs). GATs enhanced model sensitivity to crucial interactions through self-attention mechanisms. This provided a nuanced understanding of oncogenic interactions, improving the model's ability to identify significant relationships within the cancer data. GINs were used to capture complex subgraph patterns through isomorphism tests: this approach enabled precise characterization of tumor subtypes and biomarker discovery, which are essential for personalized oncology.
Our comparative analysis revealed that GNNs, with their advanced graph-based features and relational modeling capabilities, outperformed other neural network architectures in integrating multi-omics data. The superior performance of GNNs in capturing the complex, high-dimensional interactions within oncological datasets underscores their transformative potential for personalized cancer treatment strategies.
