Abstract :
Abstract Cancer is a complex and heterogeneous disease with diverse molecular profiles and clinical behaviors. Precision medicine approaches aim to tailor treatments to individual patients based on their unique genetic makeup, tumor characteristics, and clinical factors. However, the success of precision medicine hinges on accurately classifying cancer subtypes to identify optimal treatment strategies. Machine learning techniques have emerged as powerful tools for deciphering the intricate landscape of cancer subtypes and enabling personalized treatment decisions. In this study, we explore the application of machine learning algorithms for the classification of cancer subtypes, with a focus on their utility in precision medicine. By integrating multi-omics data, including genomics, transcriptomics, proteomics, and clinical variables, machine learning models can uncover hidden patterns and relationships within complex cancer datasets. These models can then classify tumors into distinct subtypes with varying prognoses and responses to treatment. We discuss recent advancements in machine learning-based cancer subtype classification and their implications for precision medicine. Furthermore, we highlight challenges and opportunities in translating these models into clinical practice, including the integration of real-world patient data and the development of interpretable models for clinical decision-making.
Keyword :
Keywords: Cancer, Precision Medicine, Machine Learning, Classification, Cancer Subtypes, Multi-omics Data