Hee June Choi

Uncovering Interpretable Fine-Grained Phenotypes of Subcellular Dynamics through Unsupervised Self-Training of Deep Neural Networks

This paper introduces DeepHACX, a self-training deep learning framework for interpretable fine-grained phenotyping of live cell dynamics, revealing specific responses to pharmacological perturbations in migrating epithelial cells.

MARS-Net: Deep learning-based segmentation pipeline for profiling cellular morphodynamics from multiple types of live cell microscopy

MARS-Net is a deep learning pipeline that improves cell edge segmentation across multiple live cell microscopy types, enabling accurate morphodynamics analysis.

Co-option of neurotransmitter signaling for inter-organismal communication in C. elegans

The paper shows that a neurotransmitter receptor in C. elegans is co-opted for inter-organismal communication via pheromone sensing.

Subcellular Time Series Modeling of Heterogeneous Cell Protrusion

The paper proposes a modeling framework to predict heterogeneous subcellular protrusion dynamics using ARX models trained on subcellular time series data from migrating cells.

Edge Detection of Cryptic Lamellipodia Assisted by Deep Learning

This paper uses deep learning to detect cryptic lamellipodia edges in fluorescence time-lapse movies of collective cell migration, improving accuracy over conventional methods.