Correlation-centred variable selection of a gene expression signature to predict breast cancer metastasis.

ABSTRACT: Predictions of distant cancer metastasis based on gene signatures are studied intensively to realise precise diagnosis and treatments. Gene selection i.e. feature selection is a cornerstone to both establish accurate predictions and understand underlying pathologies. Here, we developed a simple but robust feature selection method using a correlation-centred approach to select minimal gene sets that have both high predictive and generalisation abilities. A multiple logistic regression model was used to predict 5-year metastases of patients with breast cancer. Gene expression data obtained from tumour samples of lymph node-negative breast cancer patients were randomly split into training and validation data. Our method selected 12 genes using training data and this showed a higher area under

SUBMITTER: Hikichi S 

PROVIDER: S-EPMC7220948 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature