A highlight of this study is that the model can also predict whether a given tissue has somatic mutations in several lung cancer driver genes, including STK11, EGFR, FAT1, SETBP1, KRAS, and TP53. Note that considering the high complexity and large resources of the datasets, some studies utilized transfer learning to improve their efficiency and robustness when training new models [38], [55]. This evidence concerns the gene TP53 and lung carcinoma.