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Found 10 papers, 6 papers with code
The Artificial Intelligence Ontology: LLM-assisted construction of AI concept hierarchies
This approach not only ensures the ontology's relevance amidst the fast-paced advancements in AI but also significantly enhances its utility for researchers, developers, and educators by simplifying the integration of new AI concepts and methodologies.
Automated Annotation of Scientific Texts for ML-based Keyphrase Extraction and Validation
In this paper, we present two novel automated text labeling approaches for the validation of ML-generated metadata for unlabeled texts, with specific applications in environmental genomics.
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.
An Open-Source Knowledge Graph Ecosystem for the Life Sciences
Translational research requires data at multiple scales of biological organization.
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.
Perspectives for self-driving labs in synthetic biology
Self-driving labs (SDLs) combine fully automated experiments with artificial intelligence (AI) that decides the next set of experiments.
Creation and unification of development and life stage ontologies for animals
With the new era of genomics, an increasing number of animal species are amenable to large-scale data generation.
Guidelines for reporting cell types: the MIRACL standard
Cell types are at the root of modern biology, and describing them is a core task of the Human Cell Atlas project.
GRAPE for Fast and Scalable Graph Processing and random walk-based Embedding
Graph Representation Learning (GRL) methods opened new avenues for addressing complex, real-world problems represented by graphs.
