Ross D. King

The paper presents an AI framework integrating large language models with laboratory automation for systems biology, validated in yeast to identify novel interactions and improve hypothesis validation.

The paper develops a yeast-based platform to identify Plasmodium vivax DHS inhibitors, finding two compounds that selectively inhibit parasite DHS over human DHS.

The paper describes a genetic trap in yeast for identifying small molecule inhibitors of SARS-CoV-2 main protease, demonstrating its effectiveness with an established inhibitor.

This paper introduces NERO, a biomedical named-entity ontology with an annotated corpus, and demonstrates its utility through text embedding for discovering associations.

This paper evaluates machine learning methods for phenotype prediction in yeast, rice, and wheat, finding that standard ML methods often outperform classical statistical genetics approaches.

This study uses marker stability to predict agronomic traits and perform association mapping in rice landraces, showing improved performance over GEMMA for simulated traits and identifying markers in known QTL regions.