Improving Model Performance on the Stratification of Breast Cancer Patients by Integrating Multiscale Genomic Features.

ABSTRACT: In clinical cancer research, it is a hot topic on how to accurately stratify patients based on genomic data. With the development of next-generation sequencing technology, more and more types of genomic features, such as mRNA expression level, can be used to distinguish cancer patients. Previous studies commonly stratified patients by using a single type of genomic features, which can only reflect one aspect of the cancer. In fact, multiscale genomic features will provide more information and may be helpful for clinical prediction. In addition, most of the conventional machine learning algorithms use a handcrafted gene set as features to construct models, which is generally selected by a statistical method with an arbitrary cut-off, e.g., p value < 0.05. The genes in the gene set ar

SUBMITTER: Hao Y 

PROVIDER: S-EPMC7471833 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature