SHIFT: Survival Prediction from Incomplete and Heterogeneous Genomic Data

arXiv:2607.07725v1 Announce Type: new Abstract: Genomic prediction models often fail to transfer across institutions because sequencing panels differ across sites, creating structural feature missingness at deployment. Existing approaches to this challenge typically restrict analysis to genes shared across cohorts, exclude patients with incomplete profiles, or rely on test-time imputation, all of which can reduce robustness and limit the use of multi-center data. We propose Survival prediction Handling Incomplete Features using Transformer (SHIFT), a missingness-aware survival model that direc

Source: arXiv cs.LG — read the full report at the original publisher.

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