no code implementations • 5 Oct 2023 • Nicolas Matentzoglu, J. Harry Caufield, Harshad B. Hegde, Justin T. Reese, Sierra Moxon, HyeongSik Kim, Nomi L. Harris, Melissa A Haendel, Christopher J. Mungall
Here we present MapperGPT, an approach that uses LLMs to review and refine mapping relationships as a post-processing step, in concert with existing high-recall methods that are based on lexical and structural heuristics.
This is typically done as a statistical enrichment analysis that measures the over- or under-representation of biological function terms associated with genes or their properties, based on curated assertions from a knowledge base (KB) such as the Gene Ontology (GO).
1 code implementation • 5 Apr 2023 • J. Harry Caufield, Harshad Hegde, Vincent Emonet, Nomi L. Harris, Marcin P. Joachimiak, Nicolas Matentzoglu, HyeongSik Kim, Sierra A. T. Moxon, Justin T. Reese, Melissa A. Haendel, Peter N. Robinson, Christopher J. Mungall
Creating knowledge bases and ontologies is a time consuming task that relies on a manual curation.
To facilitate various downstream applications using clinical case reports (CCRs), we pre-train two deep contextualized language models, Clinical Embeddings from Language Model (C-ELMo) and Clinical Contextual String Embeddings (C-Flair) using the clinical-related corpus from the PubMed Central.
Clinical case reports are written descriptions of the unique aspects of a particular clinical case, playing an essential role in sharing clinical experiences about atypical disease phenotypes and new therapies.
There has been a steady need in the medical community to precisely extract the temporal relations between clinical events.