Figure 3.

Omics analysis in lung cancer studies

Different sequencing techniques allow for the simultaneous measurement of multiple molecular features of a biological sample. To improve efficiency and reduce overfitting, statistical and ML tools perform differential analysis or feature selection. Further ML models concatenate the obtained omics features with clinical features as input for lung cancer diagnostic/prognostic prediction. DEG, differentially expressed gene; RFE, recursive feature elimination; UAF, univariate association filtering.