Accurate prognosis for localized prostate cancer through coherent voting networks with multi-omic and clinical data.

ABSTRACT: Localized prostate cancer is a very heterogeneous disease, from both a clinical and a biological/biochemical point of view, which makes the task of producing stratifications of patients into risk classes remarkably challenging. In particular, it is important an early detection and discrimination of the indolent forms of the disease, from the aggressive ones, requiring post-surgery closer surveillance and timely treatment decisions. This work extends a recently developed supervised machine learning (ML) technique, called coherent voting networks (CVN) by incorporating a novel model-selection technique to counter the danger of model overfitting. For the challenging problem of discriminating between indolent and aggressive types of localized prostate cancer, accurate prognostic prediction of

SUBMITTER: Pellegrini M 

PROVIDER: S-EPMC10185505 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature