Mohammed Eslami

The paper introduces a toolkit using machine learning to predict ice binding protein potential, activity, and expression from sequences, achieving high accuracy and scalability.

This paper uses large language models to standardize metadata across biomedical datasets, achieving high accuracy in ontology alignment and reducing standardization labor.

This paper introduces DART, a DBTL loop for synthetic genetic circuit design, showing that complex circuits can be more robust and reproducible than simpler ones in budding yeast.

The paper introduces a machine learning model to predict whole-cell transcriptional responses to combinations of perturbations using data from single inducers.