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Evaluation of Artificial Intelligence (AI)-based <i>in silico</i> tools for variant classification in clinically actionable NSCLC variants

2024-04-14

Abstract excerpt

<h4>Introduction/Background</h4> Genetic variants beyond FDA-approved drug targets are often identified in non-small cell lung cancer (NSCLC) patients. Although the performances of in silico tools in predicting variant pathogenicity have been analyzed in previous studies, they have not been analyzed for actionable targets of FDA-approved therapies for NSCLC. The aim of this study is to compare the performance of...

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Literature Corpus work
0f7dc144-781c-52f6-b7c6-d85f93c5fa14
DOI
10.1101/2024.04.12.24305738
Open publication

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