10.2肺鳞状细胞癌.zip

We interrogated multi-omics data from The Cancer Genome Atlas (TCGA) and other public databases including genomic sequencing, gene expression, miRNA expression protein expression and metabolite profiles of lung cancer patients. In our study, different ML algorithms including random forest, support vector machines, neural networks and deep learning models were used to construct predictive models for diagnosing lung cancer disease, treatment response and prognosis.