Evaluation of a genetic risk score computed using human chromosomal-scale length variation to predict breast cancer

ABSTRACT:

Introduction

The ability to accurately predict whether a woman will develop breast cancer later in her life, should reduce the number of breast cancer deaths. Different predictive models exist for breast cancer based on family history, BRCA status, and SNP analysis. The best of these models has an accuracy (area under the receiver operating characteristic curve, AUC) of about 0.65. We have developed computational methods to characterize a genome by a small set of numbers that represent the length of segments of the chromosomes, called chromosomal-scale length variation (CSLV).

Methods

We built machine learning models to differentiate between women who had breast cancer and women who did not based on their CSLV characterization. We applied this procedure to two different d

SUBMITTER: Ko C 

PROVIDER: S-EPMC10273758 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature