The disentangled representations learned by DRIM can be instrumental in uncovering novel biomarkers and treatment pathways by facilitating a more nuanced understanding of the underlying biological processes associated with different modalities. By separating shared patient-related information from modality-specific details, researchers can identify unique features that may correlate with specific tumor characteristics or treatment responses. For instance, the unique representations derived from genomic data could reveal genetic alterations that are not apparent when analyzing data in a fused manner.
Moreover, the shared representations can be utilized to perform clustering analyses, enabling the identification of patient subgroups with similar prognostic profiles or treatment responses. This clustering can lead to the discovery of novel biomarkers that are predictive of patient outcomes or therapeutic efficacy. Additionally, the dual-scale fusion approach employed in DRIM allows for the integration of these insights into predictive models, potentially guiding personalized treatment strategies. By leveraging the attention mechanisms within the MAFusion framework, clinicians can also gain interpretability into which modalities contribute most significantly to patient outcomes, thereby informing targeted interventions.