A machine learning approach to predict cellular mechanical stresses in response to chemical perturbation.

ABSTRACT: Mechanical stresses generated at the cell-cell level and cell-substrate level have been suggested to be important in a host of physiological and pathological processes. However, the influence various chemical compounds have on the mechanical stresses mentioned above is poorly understood, hindering the discovery of novel therapeutics, and representing a barrier in the field. To overcome this barrier, we implemented two approaches: 1) monolayer boundary predictor and 2) discretized window predictor utilizing either stepwise linear regression or quadratic support vector machine machine learning model to predict the dose-dependent response of tractions and intercellular stresses to chemical perturbation. We used experimental traction and intercellular stress data gathered from samples subject

SUBMITTER: SubramanianBalachandar V 

PROVIDER: S-EPMC10502424 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature