Biomarker Discovery

The main practical implication is that iterative dual-criterion selection can be made feasible for high-dimensional biomarker discovery while keeping panels compact. The RNA analysis identified 617 LUAD-specific genes and 1,086 LUAD-dominant shared genes. The methylation-expression integration categorized 388 entries as likely tumor suppressors and 262 as likely activated oncogenes. Top QNN feature importance results included genes such as PAK7, ADAM23, DMRT2, PRSS12, EFNB1, SYN2, NGFR, IRS4, BEX1, and PDPN. Hypermethylation with downregulation was interpreted as likely epigenetic silencing. Hypomethylation with upregulation was interpreted as likely activation. Dual-criterion accumulation is framed as central because it requires both directional coefficient consistency and recurring selection. Discovery platforms include antigen arrays, proteomics, RNA sequencing and soluble immune factor profiling. High-dimensional genomic classification makes biomarker discovery vulnerable to unstable feature selection because many feature subsets can perform similarly. The cohort and biobank are intended to support discovery and validation of biomarkers for an accurate, accessible acute rheuma…