Abstract
The identification of cancer-associated genes is a critical step in understanding the molecular mechanisms underlying cancer and in developing targeted therapies. Computational tools have become essential in this process by enabling the analysis of large-scale genomic data to identify potential cancer driver genes. This article reviews the computational methods used to identify cancer-associated genes, focusing on tools for genome-wide association studies (GWAS), next-generation sequencing (NGS) data analysis, and gene expression profiling. We also discuss the challenges and future directions in cancer gene identification, including the integration of multi-omics data and the application of machine learning techniques.
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