Yasushi Okochi

This paper introduces scDIVIDE, a framework to infer division rates and cellular dynamics from time-series scRNA-seq data, showing it accurately captures birth rates in biological processes.

This study uses a computational model and machine learning to analyze monkey decision-making in risk-taking tasks, revealing dynamic optimism and pessimism states and a unique control rule for bias.

This paper introduces a zero-shot framework to predict spatial transcriptomes of mutants using single-cell RNA sequencing data without requiring spatial reference atlases.

The paper presents a single-cell transcriptome atlas of Drosophila gastrulae, identifying 65 clusters and revealing spatial gene expression patterns across germ layers.

Perler predicts spatial gene expression from scRNA-seq data by integrating with in situ hybridization data using a generative linear model, achieving accurate and interpretable results across multiple tissues.