Naonori Ueda

The paper identifies non-linear gene sets from blood cells to classify ALS with high accuracy using machine learning and validates findings in lab samples and iPSC-derived motor neurons.

This study develops a deep learning method to automatically extract explainable features from histopathological images, achieving higher accuracy than human pathologists in predicting prostate cancer recurrence.