Explainable Artificial Intelligence for Predicting Oncogenic Gene Expression Programs Driven by MYC Condensate Dynamics
Keywords:
explainable artificial intelligence, MYC condensate dynamics, oncogenic gene expression, phase separation, system architecture, algorithmic governance, robustness, fairness, infrastructureAbstract
The emergence of phase separation as a fundamental organizing principle in transcriptional regulation has reshaped our understanding of oncogenic gene expression, particularly for the MYC oncoprotein whose condensate dynamics orchestrate selective transcriptional programs. Concurrently, explainable artificial intelligence has become indispensable for interpreting high-dimensional genomic data and for building trust in predictive models used in clinical and research settings. This paper presents a systems-level analysis of an explainable AI framework designed to predict oncogenic gene expression programs driven by MYC condensate dynamics. We examine the architectural trade-offs between predictive accuracy and interpretability, the robustness of model explanations under biological variability, and the infrastructural requirements for deploying such systems in translational workflows. Governance and fairness considerations are discussed through the lens of algorithmic bias in genomics, reproducibility of explanations, and ethical implications for personalized oncology. Cross-domain comparisons with other biophysical systems, such as transcriptional condensates at super-enhancers, highlight the generality of the framework. Sustainability of model training, data provenance, and the policy landscape for responsible AI in molecular biology are also addressed. The paper argues that while explainable AI offers powerful tools for deciphering complex phase-separation-driven transcription, its effective integration demands careful attention to system architecture, validation protocols, and socio-technical governance to ensure reliable and equitable outcomes.
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