Biomedical Text Mining and Ontologies

Assembly and reasoning over semantic mappings at scale for biomedical data integration

The paper introduces SeMRA, a tool for integrating biomedical identifier mappings across resources, enabling large-scale data interoperability.

Extracting the Research Goal from Biomedical Abstracts

The paper fine-tunes a model to extract four research goal criteria from biomedical abstracts using the RG4C dataset, achieving an overall F-score of 0.59.

Medical Abbreviation Disambiguation with Large Language Models: Zero- and Few-Shot Evaluation on the MeDAL Dataset

This study evaluates zero- and few-shot performance of large language models on medical abbreviation disambiguation using the MeDAL dataset, showing GPT-4 outperforms LLaMA.

Mining the neuroimaging literature

The paper provides tools for collecting, processing, and annotating biomedical literature to streamline text-mining and meta-science projects.

Dual-LLM Adversarial Framework for Information Extraction from Research Literature

The paper introduces a dual-LLM adversarial framework to improve information extraction from scientific texts, showing better performance than single-LLM and manual methods.

EAGLE-AI: A large language model workflow for automated extraction and scoring of literature evidence linking genes to autism spectrum disorder

EAGLE-AI automates gene-autism linkage evidence extraction and scoring with high accuracy, though challenges remain in handling unscreened papers and supplementary materials.

CIViC-Fact: a proof-of-concept framework for AI-assisted verification of cancer variant interpretations

CIViC-Fact evaluates AI models for verifying cancer variant interpretations using full-text evidence, showing abstracts are insufficient for accurate validation.

Biomedical text corpora for multi-entity recognition in diet-related metabolic syndrome research

The paper introduces five biomedical corpora for multi-entity recognition in metabolic syndrome research, including human-annotated and machine-annotated datasets with diverse entity categories.

Instruction-tuned extraction of virus-host interactions from integrated scientific evidence

This study develops an instruction-tuned LLM for extracting virus-host interactions from full-text biomedical articles, showing improved performance under low-resource conditions.

A Semantic Framework for Predicting Herbal-Drug Biotransformation Conflicts via Biomedical Literature Mining

This paper develops a semantic framework to predict herbal-drug biotransformation conflicts by mining biomedical literature and constructing a knowledge graph of interactions.

Large Language Models Can Extract Metadata for Annotation of Human Neuroimaging Publications

This paper demonstrates that large language models can effectively extract metadata from neuroimaging publications with performance comparable to human annotators.

Development of an Application Ontology for Beta Cell Genomics Based On the Ontology for Biomedical Investigations

The paper describes the development of a beta cell genomics application ontology (BCGO) based on the Ontology for Biomedical Investigations, validated through data annotation, queries, and automated classification.

Improving Automated Deep Phenotyping Through Large Language Models Using Retrieval Augmented Generation

RAG-HPO improves HPO term assignment using retrieval-augmented generation, achieving higher precision and recall than existing tools in phenotypic analysis of clinical text.

Accelerating Insight Discovery in Large Biomedical Text with Scalable Processing Framework

The paper introduces Quanta, a hybrid system combining large language models with evaluation metrics to improve accuracy and efficiency in processing biomedical text for dermatology.

The Data Distillery: A Graph Framework for Semantic Integration and Querying of Biomedical Data

The paper introduces a knowledge graph framework for integrating and querying biomedical data across domains, demonstrating its utility in eight use cases.

From literature to biodiversity data: mining arthropod organismal and ecological traits with machine learning

This paper uses machine learning to extract arthropod trait data from literature, creating a database to support biodiversity research.

MilkOligoCorpus: a semantically annotated resource for knowledge extraction on mammalian milk oligosaccharides

This paper introduces MilkOligoCorpus, a manually annotated corpus for extracting structured data on milk oligosaccharides, along with annotation guidelines and terminological resources.

MicrobioRel: A Set of Datasets for Microbiome Relation Extraction

The paper introduces MicrobioRel, two datasets for microbiome relation extraction, and evaluates models for this task, finding PubMedBERT to be effective.

Identification and characterisation of bacterial pathogens through Large Language Model-assisted text mining

This paper uses a large language model to mine biomedical literature and identify bacterial pathogens, resulting in a catalog of 1,222 species with human infection evidence.

Unraveling the Co-Morbidity between COVID-19 and Neurodegenerative Diseases Through Multi-scale Graph Analysis: A Systematic Investigation of Biological Databases and Text Mining

An Open-Set Semi-Supervised Multi-Task Learning Framework for Context Classification in Biomedical Texts

The paper introduces CELESTA, a framework for biomedical context classification that improves accuracy by integrating semi-supervised multi-task learning and a large-scale annotated dataset.

Can large language models reliably extract human disease genes from full-text scientific literature?

This paper benchmarks three LLMs for extracting gene-disease-phenotype relationships from biomedical texts, finding GPT-4o achieves 88.8% accuracy using zero-shot prompting.

Reading papers: Extraction of molecular interaction networks with large language models

The paper uses large language models to extract molecular interaction networks from scientific texts, achieving high precision and recall in protein-protein and gene regulatory network relations.

VaxKG: Integrating The Vaccine Ontology And VIOLIN For Advanced Vaccine Queries And LLM-Powered Chat Systems

This paper develops VaxKG, a vaccine knowledge graph integrating VIOLIN and VO, enabling complex queries and LLM-powered chat systems for vaccine information.

Mapping and Harmonization of CVX vaccine terms tothe Vaccine Ontology

The paper maps CVX vaccine codes to the Vaccine Ontology using semi-automated and manual methods, achieving full coverage and improving semantic vaccine classification.

PlantConnectome: knowledge graph encompassing >70,000 plant articles

PlantConnectome extracts 5 million functional relationships from 71,000 plant biology abstracts using large language models and provides a database for gene-metabolite interactions and regulatory networks.

Construction, Deployment, and Usage of the Human Reference Atlas Knowledge Graph for Linked Open Data

The paper introduces the Human Reference Atlas Knowledge Graph, a large-scale linked data resource for biological entities, and describes its construction, deployment, and usage.

Extracting knowledge from scientific texts on patient-derived cancer models using Large Language Models: algorithm development and validation

This study develops and validates LLM-based methods to extract PDCM-related entities from scientific texts, showing soft prompting improves small open LLMs' performance.

AOP-helpFinder 3.0: from text mining to network visualization of key event relationships, and knowledge integration from multi-sources.

AOP-helpFinder 3.0 integrates toxicological data from multiple sources to identify and visualize key event relationships, enhancing adverse outcome pathway development.

PloverDB: A high-performance platform for serving biomedical knowledge graphs as standards-compliant web APIs

PloverDB provides a platform to serve biomedical knowledge graphs as standards-compliant web APIs, enhancing data accessibility and interoperability.