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

• Published in: Scientific reports Vol. 11; no. 1; p. 14914
• Main Authors: Cheng, Li-Hsin, Hsu, Te-Cheng, Lin, Che
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 21.07.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/114/1305; 631/114/2164; 631/114/2407; 631/553; 631/67; 631/67/1347; 692/53; 692/53/2422; Biomarkers; Biomarkers, Tumor - genetics; Breast cancer; Breast Neoplasms - diagnosis; Breast Neoplasms - genetics; Breast Neoplasms - mortality; Deep Learning; DNA microarrays; Female; Forecasting; Genes; Humanities and Social Sciences; Humans; Medical prognosis; multidisciplinary; Neural networks; Neural Networks, Computer; Precision medicine; Prediction models; Prognosis; Science; Science (multidisciplinary); Support Vector Machine; Survival Analysis; Systems Biology - methods

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. Howe...