Shangying Wang

This paper develops a foundation model for microbial growth dynamics using self-supervised learning on diverse datasets, enabling accurate reconstruction and prediction of growth patterns and antibiotic responses.

The paper uses synthetic costimulatory domains and machine learning to identify design rules for CARs that enhance cytotoxicity and stemness.

This study develops a deep learning model to predict phenotype probability distributions from noisy biological data with few observations per condition.

The paper demonstrates a method using self-organized patterns and machine learning to encode and decode information, showing scalability by mapping the English keyboard.

The paper introduces a framework using neural networks to accelerate computational simulations of biological models by emulating their behavior across larger parametric spaces.