Richard Fernandes
The advent of precision medicine has revolutionized oncology by promising tailored therapeutic strategies based on individual patient characteristics. Central to this advancement is the integration of multi-omics data—genomics, transcriptomics, proteomics, and metabolomics— providing a comprehensive understanding of cancer's molecular underpinnings. This study explores the integration of machine learning algorithms for predictive modeling of drug response in cancer patients using a multi-omics approach. By leveraging advanced computational techniques and vast multi-omics datasets, the research aims to enhance the accuracy and efficacy of predicting patient-specific responses to cancer treatments, thereby facilitating personalized medicine. Key challenges such as cancer heterogeneity, high dimensionality of data, and integration of disparate data types are addressed using multi-view learning, data integration frameworks, and feature fusion strategies. Explainable AI methods are employed to interpret the models and uncover potential biomarkers and therapeutic targets. The ultimate goal is to develop a predictive modeling framework for clinical use, guiding treatment decisions and improving patient outcomes.
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