Nen Saito

Decoding Anadara shell morphology with deep learning

This paper introduces Morpho-VAE, a deep learning framework for analyzing shell morphology, which outperforms traditional methods in species classification and reveals morphological patterns in Anadara shells.

Filament-resolved simulations reproduce self-organization of lamellipodia and filopodia

The paper develops a filament-resolved model of actin self-organization, showing how Arp2/3 and fascin concentrations determine lamellipodia, filopodia, and reticulated meshwork morphologies.

Emergence of Bow-tie Architecture in Evolving Feedforward Networks

This paper shows that bow-tie architecture emerges in evolving feedforward networks due to weak initial molecular interactions, not adaptive advantages.

A method for morphological feature extraction based on variational auto-encoder : an application to mandible shape

The paper introduces Morpho-VAE, a landmark-free method for shape analysis using variational autoencoders, applied to primate mandible images to extract morphological features for classification.