Knowledge-Guided Deep Learning Framework for Linking Medical Image Phenotypes and YAP-MAML2–Associated Transcriptional Dynamics in Cancer Progression Analysis - Pascal Ortiz; Felix C. Simpson (International Journal of Clinical and Translational Medicine, 2026)
The integration of medical imaging and molecular profiling has been heralded as a cornerstone of precision oncology, yet the systems-level coupling between radiographic phenotypes and the transcriptional dynamics of oncogenic fusions remains poorly characterized. This paper presents a knowledge-guided deep learning framework designed to bridge the gap between image-based tumor phenotypes and the gene regulatory programs driven by the YAP-MAML2 fusion protein. The framework embeds curated pathway topologies, chromatin interaction maps, and condensate-mediated transcriptional models directly into a multimodal neural architecture, enforcing biological consistency while learning representations that connect morphological heterogeneity to downstream transcriptional trajectories. By structuring the architecture as a dual-stream system with shared latent alignments and constraint layers, the design hard-codes domain knowledge and offers an interpretable scaffold for probing the link between phenotypic imaging patterns and phase-separated transcriptional condensates. Drawing on lessons from large-scale biomedical AI deployments, the discussion extends beyond model design to encompass structural trade-offs, data governance, federated infrastructure, fairness, and long-term sustainability. The analysis underscores that the clinical translation of such knowledge-augmented systems depends as much on institutional interoperability, algorithmic equity, and regulatory alignment as on predictive accuracy. This work provides a systems-oriented blueprint for constructing, validating, and governing integrative deep learning systems that respect the complexity of both molecular biology and healthcare delivery.