Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

ABSTRACT: Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), pr

SUBMITTER: Alakwaa FM 

PROVIDER: S-EPMC5759031 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature