113 citations · 153 across the 7 of their papers we have counts for
7 papers
SchemaLink: An Intelligent Web Editor for LinkML Schema Curation
Emanuele Cavalleri, Paolo Perlasca, J. Harry Caufield +3
Motivation: LinkML is a suitable language for the representation of the structural and content constraints of different kinds of biomedical data. Even if it is a quite recent propo…
RNA-KG: An ontology-based knowledge graph for representing interactions involving RNA molecules
Emanuele Cavalleri, Alberto Cabri, Mauricio Soto-Gomez +9
The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to…
MapperGPT: Large Language Models for Linking and Mapping Entities
Nicolas Matentzoglu, J. Harry Caufield, Harshad B. Hegde +6
Aligning terminological resources, including ontologies, controlled vocabularies, taxonomies, and value sets is a critical part of data integration in many domains such as healthca…
An evaluation of GPT models for phenotype concept recognition
Tudor Groza, Harry Caufield, Dylan Gration +5
Objective: Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-trac…
An Open-Source Knowledge Graph Ecosystem for the Life Sciences
Tiffany J. Callahan, Ignacio J. Tripodi, Adrianne L. Stefanski +29
Translational research requires data at multiple scales of biological organization. Advancements in sequencing and multi-omics technologies have increased the availability of these…
Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning
J. Harry Caufield, Harshad Hegde, Vincent Emonet +9
Creating knowledge bases and ontologies is a time consuming task that relies on a manual curation. AI/NLP approaches can assist expert curators in populating these knowledge bases,…
GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding
Luca Cappelletti, Tommaso Fontana, Elena Casiraghi +8
Graph Representation Learning (GRL) methods opened new avenues for addressing complex, real-world problems represented by graphs. However, many graphs used in these applications co…