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This article reviews the integration of biomedical knowledge into machine learning (ML) models for cancer diagnosis and prognosis, termed knowledge-informed machine learning (KIML). It discusses the challenges faced by ML in cancer applications, such as limited labeled data, high-dimensionality, and the need for interpretability, while highlighting the potential benefits of incorporating domain knowledge. The review also outlines various data types, knowledge representation methods, and integration strategi

Author

kalpanadhamodharan22

Language

EN