Machine learning-informed liquid-liquid phase separation for personalized breast cancer treatment assessment.

ABSTRACT:

Background

Breast cancer, characterized by its heterogeneity, is a leading cause of mortality among women. The study aims to develop a Machine Learning-Derived Liquid-Liquid Phase Separation (MDLS) model to enhance the prognostic accuracy and personalized treatment strategies for breast cancer patients.

Methods

The study employed ten machine learning algorithms to construct 108 algorithm combinations for the MDLS model. The robustness of the model was evaluated using multi-omics and single-cell data across 14 breast cancer cohorts, involving 9,723 patients. Genetic mutation, copy number alterations, and single-cell RNA sequencing were analyzed to understand the molecular mechanisms and predictive capabilities of the MDLS model. Immunotherapy targets were predicted by evaluat

SUBMITTER: Wang T 

PROVIDER: S-EPMC11611825 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature