Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.

ABSTRACT: The vast amount of available sequencing data allows the scientific community to explore different genetic alterations that may drive cancer or favor cancer progression. Software developers have proposed a myriad of predictive tools, allowing researchers and clinicians to compare and prioritize driver genes and mutations and their relative pathogenicity. However, there is little consensus on the computational approach or a golden standard for comparison. Hence, benchmarking the different tools depends highly on the input data, indicating that overfitting is still a massive problem. One of the solutions is to limit the scope and usage of specific tools. However, such limitations force researchers to walk on a tightrope between creating and using high-quality tools for a specific purpose and

SUBMITTER: Nourbakhsh M 

PROVIDER: S-EPMC10805075 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature