Machine learning based methodology to identify cell shape phenotypes associated with microenvironmental cues.

ABSTRACT: Cell morphology has been identified as a potential indicator of stem cell response to biomaterials. However, determination of cell shape phenotype in biomaterials is complicated by heterogeneous cell populations, microenvironment heterogeneity, and multi-parametric definitions of cell morphology. To associate cell morphology with cell-material interactions, we developed a shape phenotyping framework based on support vector machines. A feature selection procedure was implemented to select the most significant combination of cell shape metrics to build classifiers with both accuracy and stability to identify and predict microenvironment-driven morphological differences in heterogeneous cell populations. The analysis was conducted at a multi-cell level, where a "supercell" method used average

SUBMITTER: Chen D 

PROVIDER: S-EPMC11305428 | biostudies-literature | 2016 Oct

REPOSITORIES: biostudies-literature