Interpretable Fine-Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning.

ABSTRACT: Uncovering fine-grained phenotypes of live cell dynamics is pivotal for a comprehensive understanding of the heterogeneity in healthy and diseased biological processes. However, this endeavor poses significant technical challenges for unsupervised machine learning, requiring the extraction of features that not only faithfully preserve this heterogeneity but also effectively discriminate between established biological states, all while remaining interpretable. To tackle these challenges, a self-training deep learning framework designed for fine-grained and interpretable phenotyping is presented. This framework incorporates an unsupervised teacher model with interpretable features to facilitate feature learning in a student deep neural network (DNN). Significantly, an autoencoder-based regul

SUBMITTER: Wang C 

PROVIDER: S-EPMC11538677 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature