For example, the EGFR mutation profile of cell lines was used by Guan et al. [14] to investigate the relationship between gene mutations and response to Lapatinib (EGFR inhibitor), and found that cell lines with EGFR mutations were more sensitive to Lapatinib on both predicted and observed data; Chiu et al. [15] designed a deep learning model and predicted that Afatinib and Gefitinib, two inhibitors of EGFR, performed better in NSCLC with EGFR mutation than without EGFR mutation by using a large number of tumor samples from the Cancer Genome Atlas (TCGA) database. This evidence concerns the gene EGFR and non-small cell lung carcinoma.