NIH R01 · 2025
Molecular features associated with time-to-event outcomes, such as overall or disease-free survival, may be prognostically relevant or potential therapeutic targets. Therefore, analyzing data from high-throughput genomic assays with clinical follow-up data has been of growing interest. The Cancer Genome Atlas (TCGA) Project has collected baseline demographic, clinical characteristics, and follow-up data for 11,125 patients for 32 different cancer types and corresponding tissue samples were processed for examining SNPs, copy number, methylation, miRNA expression, and mRNA expression. Because the number of variables (P ) exceeds the sample size (N), one strategy frequently employed when…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.