Topical hidden genome: discovering latent cancer mutational topics using a Bayesian multilevel context-learning approach.

ABSTRACT: Inferring the cancer-type specificities of ultra-rare, genome-wide somatic mutations is an open problem. Traditional statistical methods cannot handle such data due to their ultra-high dimensionality and extreme data sparsity. To harness information in rare mutations, we have recently proposed a formal multilevel multilogistic "hidden genome" model. Through its hierarchical layers, the model condenses information in ultra-rare mutations through meta-features embodying mutation contexts to characterize cancer types. Consistent, scalable point estimation of the model can incorporate 10s of millions of variants across thousands of tumors and permit impressive prediction and attribution. However, principled statistical inference is infeasible due to the volume, correlation, and noninterpretabi

SUBMITTER: Chakraborty S 

PROVIDER: S-EPMC11056772 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature