Identification and Validation of Cancer Mutations Using Computational Approaches: A Review
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Abstract
Abstract
Cancer is a genetic disease driven by somatic mutations, with a subset of these mutations acting as drivers to promote tumorigenesis. Identifying and validating these driver mutations is essential for understanding cancer biology and developing targeted therapies. With the explosion of genomic data from large-scale sequencing projects, computational approaches have become indispensable tools for analyzing these data, predicting functional mutations, and distinguishing driver mutations from passengers. This review provides an overview of key computational methods for cancer mutation analysis, including variant calling, driver mutation identification, machine learning, and network-based approaches. It discusses current challenges, the application of these methods, and future directions, emphasizing the integration of multi-omics data and Artificial Intelligence (AI) to drive advancements in cancer research and personalized medicine.
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