Yuta Tokuoka

This paper introduces a deep learning model to predict gene regulation relationships for both known and unknown transcription factors, achieving higher accuracy for unknown TFs than existing methods.

This paper introduces an unsupervised domain adaptation method for cell segmentation that avoids predefined thresholds, achieving accuracy comparable to supervised models without labeled data.

This paper introduces MaLoN, an unsupervised method for inferring gene regulatory networks from in vivo data, showing improved accuracy over existing methods in yeast and E. coli.

This paper develops a deep learning model to automatically predict mouse seminiferous tubule stages from bright-field microscopy images, achieving 98.33% accuracy with a ±1 stage tolerance.

The paper develops FL2-Net, a deep learning method for nuclear segmentation in bright-field microscopy images of mouse embryos, achieving higher birth prediction accuracy than existing methods and experts.

The paper develops a deep learning algorithm to predict mouse embryo live birth potential from nuclear structural features in fluorescence images, achieving 83.87% accuracy.

This paper introduces a deep learning-based instance segmentation algorithm, QCA Net, which outperforms existing methods in segmenting nuclei in mouse embryos and provides quantitative developmental metrics.

This study uses convolutional neural networks to predict future cell movement direction from current cell shape images with high accuracy.