Lawrence Hunter

KG-Microbe constructs modular knowledge graphs to integrate microbial data, enabling analysis of complex interactions in health and environment.

The paper uses a knowledge graph to hypothesize mechanistic links between microbes and disease by analyzing relationships between microbes, genes, and metabolites.

The paper evaluates knowledge graphs for mechanistic inference of gut microbiome-disease relationships and highlights the need for semantic constraints and integrated resources.

The paper introduces an ignorance-base, a knowledge base of known unknowns in prenatal nutrition literature, to identify new research avenues by analyzing ignorance statements and biomedical concepts.

This paper explores sequence-to-sequence machine translation for concept recognition in biomedical text, achieving state-of-the-art results on the CRAFT Corpus with lower computational demands.

The paper analyzes linguistic features of bioRxiv preprints compared to published biomedical texts and develops tools to link preprints to peer-reviewed articles and journals.

The paper introduces a method to identify and classify ignorance statements in biomedical text, creating a gold standard corpus and classifiers with high F1 scores.

The paper introduces MechSpy, a framework that generates mechanistic hypotheses of toxicity by combining gene expression data with biological knowledge, and validates predictions experimentally.

The paper introduces PheKnowLator, a framework for constructing large-scale biomedical knowledge graphs, and evaluates its performance on human disease mechanisms.

The paper semantically integrates LOINC-encoded laboratory data with the Human Phenotype Ontology to identify asthma biomarkers from EHR data.

The paper improves biomedical concept recognition using deep neural sequence models, achieving higher F1 scores for ontology concept identification in scientific texts.