Integrating ensemble systems biology feature selection and bimodal deep neural network for breast cancer prognosis prediction.

ABSTRACT: Breast cancer is a heterogeneous disease. To guide proper treatment decisions for each patient, robust prognostic biomarkers, which allow reliable prognosis prediction, are necessary. Gene feature selection based on microarray data is an approach to discover potential biomarkers systematically. However, standard pure-statistical feature selection approaches often fail to incorporate prior biological knowledge and select genes that lack biological insights. Besides, due to the high dimensionality and low sample size properties of microarray data, selecting robust gene features is an intrinsically challenging problem. We hence combined systems biology feature selection with ensemble learning in this study, aiming to select genes with biological insights and robust prognostic predictive power

SUBMITTER: Cheng LH 

PROVIDER: S-EPMC8295302 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature