Yuichiro Yada

InferAging quantifies biological aging by modeling deviations from chronological age, identifying accelerated and delayed agers in zebrafish using non-invasive phenotypes.

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.

Monju performs multi-criteria clustering on single-cell omics data by integrating multiple clustering solutions to assess cluster stability.

This study uses a Bayesian hierarchical model to decode decision-making properties from trust game data, identifying biomarkers linked to trusting behavior in MDD patients and healthy controls.

SPERRFY applies Sperry's chemoaffinity theory to whole-brain connectivity using spatial transcriptomics and connectomic data, revealing positional gradients that predict axonal wiring with high accuracy.

This paper develops a hierarchical Bayesian model to predict amyloid-beta accumulation from biomarker candidates using mouse behavioral data, showing improved prediction when multiple features are used.