1 citations · 1 across the 3 of their papers we have counts for
4 papers
Dynamical Regimes of Discrete Diffusion Models
Tomoei Takahashi, Takashi Takahashi, Yoshiyuki Kabashima
Diffusion models generate high-dimensional data such as images by learning a process that gradually removes noise from corrupted data. Recent studies have shown that the backward d…
Alpha helices are more evolutionarily robust to environmental perturbations than beta sheets: Bayesian learning and statistical mechanics for protein evolution
Tomoei Takahashi, George Chikenji, Kei Tokita +1
How typical elements that shape organisms, such as protein secondary structures, have evolved, or how evolutionarily susceptible/resistant they are to environmental changes, are si…
The cavity method to protein design problem
Tomoei Takahashi, George Chikenji, Kei Tokita
In this study, we propose an analytic statistical mechanics approach to solve a fundamental problem in biological physics called protein design. Protein design is an inverse proble…
Lattice protein design using Bayesian learning
Tomoei Takahashi, George Chikenji, Kei Tokita
Protein design is the inverse approach of the three-dimensional (3D) structure prediction for elucidating the relationship between the 3D structures and amino acid sequences. In ge…