Machine learning on transcription factor expression profiles for precision breast cancer therapy.

ABSTRACT:

Background

Although breast cancer is a significant heterogeneous disease with an increasing global prevalence, precise prognostic evaluation is a vital aspect of designing personalized therapy strategies and upholding patients' survival rates. With the incorporation of artificial intelligence technology, in particular, machine learning, cancer prognosis and prediction have significantly been redefined.

Methods

In this study, we adopted a ten-fold cross-validation method to construct a Machine Learning-Derived Transcription Factor Signature (MDTS) across 108 algorithmic combinations. The optimal model was selected based on the highest average C-index across ten cohorts. We integrated single-cell data with multi-omics analysis to comprehensively assess the robustness of the MD

SUBMITTER: Zhang X 

PROVIDER: S-EPMC12512818 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature