Noriko F. Hiroi

This study uses a Transformer-based model to predict astrocyte migration during mouse brain development, finding that environmental information and multimodal modeling improve prediction accuracy.

TabularQual provides a spreadsheet-based format for annotating logical models, with tools to convert between spreadsheets and SBML-qual, enhancing model reuse and interoperability.

The paper describes the development and evaluation of an agentic framework using LLMs to explore molecular interaction maps through user-driven prototyping and testing.

The paper simulates cytokinesis by modeling cytoplasmic flow and cortical elasticity interactions, showing spindle-driven flow creates a pressure minimum that initiates cleavage furrow formation.

The paper presents a computational framework for analyzing SARS-CoV-2-host interactions to identify drug targets and model disease mechanisms.

The paper describes the development of a computational knowledge repository, the COVID-19 Disease Map, which curates SARS-CoV-2 virus-host interaction mechanisms and provides tools for their analysis and application.

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.

This study uses CAGE-seq to analyze transcriptome changes in Pv11 cells during desiccation and rehydration, identifying DEGs and suggesting DNA repair as a recovery mechanism.