Moreover, unlike previous studies that relied on single data sources, this research demonstrates the potential of integrating diverse biomedical data types to enhance the predictive power of models, highlighting an important trend in contemporary cancer research.
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Machine learning-based multimodal radiomics and transcriptomics models for predicting radiotherapy sensitivity and prognosis in esophageal cancer.
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