Fu Xiaozhi
Department of Life Sciences, Chalmers University of Technology, Kemivägen 10, SE-412 96 Gothenburg, Sweden.
Patterns (N Y). 2024 Aug 9;5(8):101043. doi: 10.1016/j.patter.2024.101043.
Evolutionary-based machine learning models have emerged as a fascinating approach to mapping the landscape for protein evolution. Lian et al. demonstrated that evolution-based deep generative models, specifically variational autoencoders, can organize SH3 homologs in a hierarchical latent space, effectively distinguishing the specific Sho1 domains.
基于进化的机器学习模型已成为一种描绘蛋白质进化图景的迷人方法。Lian等人证明，基于进化的深度生成模型，特别是变分自编码器，可以在分层潜在空间中组织SH3同源物，有效区分特定的Sho1结构域。
