Lisa R. Matthews

The paper describes the development and evaluation of an agentic framework using LLMs to explore molecular interaction maps through user-driven prototyping and testing.

This paper applies large language models to assist in gene annotation into Reactome pathways, showing promising results in supporting curation workflows.

The paper annotates disease variants in the context of biological pathways to infer molecular phenotypes and model variant impacts.

This paper explores using ChatGPT in Reactome curation to automate pathway annotation, comparing its outputs to manual curation and identifying challenges and improvements.

The paper predicts functional interactions of dark proteins with Reactome pathways using a random forest classifier and develops a web portal to visualize these interactions.

The paper presents a computational framework for analyzing SARS-CoV-2-host interactions to identify drug targets and model disease mechanisms.

The paper describes the development of a computational knowledge repository, the COVID-19 Disease Map, which curates SARS-CoV-2 virus-host interaction mechanisms and provides tools for their analysis and application.