Koutarou D. Kimura

DiverScan is a deep-learning framework that uses attention mechanisms to analyze animal behavior data, identifying condition-specific features across diverse experiments.

This study examines how photothermal therapy affects ERK signaling and intercellular communication in tumor spheroids using FRET sensors and deep learning.

The paper harmonizes whole-brain imaging datasets from multiple labs to enable automated neuron identification and identifies factors influencing neuron positioning in C. elegans.

Seg2Link provides a semi-automated tool for cell segmentation in 3D image stacks, combining deep learning predictions with manual corrections to improve accuracy and efficiency.

This study identifies how prior experience modulates sensory gain control in C. elegans ASH neurons, altering their response to odor concentration changes and enhancing odor avoidance behavior.

This study introduces a red/far-red light-responsive optogenetic system in C. elegans to manipulate cell signaling without blue light avoidance behavior.

This study identifies persistent behavioral responses in C. elegans caused by electric shock, suggesting a form of fear-like emotion through genetic and behavioral analyses.

The paper develops a deep-learning-based pipeline for segmenting and tracking cells in 3D brain imaging data, achieving high accuracy across different conditions and datasets.

This paper introduces a hybrid machine learning method to estimate behavioral states and extract features from low-content animal trajectory data, validated across diverse species and environments.