Lauren Spirko‐Burns

The paper introduces methods for feature selection in genomics data with censored survival outcomes, addressing non-proportional hazards and outperforming existing approaches.

The paper develops supervised methods for feature selection and survival prediction in large-scale omics data with censored outcomes and time-varying effects using continuum power regression and accelerated failure time models.