Search Results for author:
Found 6 papers, 3 papers with code
MapperGPT: Large Language Models for Linking and Mapping Entities
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.
Gene Set Summarization using Large Language Models
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).
Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning
Creating knowledge bases and ontologies is a time consuming task that relies on a manual curation.
Clinical Named Entity Recognition using Contextualized Token Representations
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.
CREATe: Clinical Report Extraction and Annotation Technology
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.
Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference
There has been a steady need in the medical community to precisely extract the temporal relations between clinical events.
