WeiFu: A Novel Pan-Cancer Driver Gene
\nIdentification Method Using Incidence-Weighted
\nMutation Scores

Genetic and genomic variations are primary drivers of tumor development. Identifying driver
\ngenes from numerous passenger genes across pan-cancer poses a significant challenge due to varying
\nmutation loads. While independent studies have elucidated cancer-associated mutation patterns within
\nspecific cancer types, a systematic approach to integrating these mutation data for assessing the impact of
\ngene mutations has been lacking. This study addresses this gap by integrating pan-cancer genomic somatic
\nmutation data and introducing a novel mutation weight fusion (WeiFu) score calculation method. WeiFu
\ncomputes frequency and weighted fusion scores by cancer type, facilitating the identification of potential
\ndriver genes. Evaluation results on an integrated pan-cancer dataset comprising 29 different cancer types
\ndemonstrate that WeiFu significantly outperforms current well-known approaches in prediction accuracy,
\nsensitivity, and specificity. Notably, WeiFu recovers 277 known cancer genes among the top 500 ranked
\ncandidates and successfully identifies potential driver genes supported by strong evidence. Consequently,
\nWeiFu shows considerable promise for identifying driver genes within the rapidly expanding corpus of cancer
\ngenomic data.