Biomedical Text Mining and Ontologies

The paper introduces mx-plore, a database of miRNA-gene interactions derived from text mining, providing context-specific information from scientific publications.

VaxLLM automates Brucella vaccine article classification and annotation with high accuracy, outperforming non-fine-tuned models and enabling structured data integration.

This study uses Large Language Models with Retrieval-Augmented Generation to auto-annotate plant phenotypes with ontology terms, achieving results comparable to expert annotations.

The paper describes the transformation of multiomics and EHR data into privacy-preserving biomedical knowledge graphs within the Biomedical Data Translator ecosystem.

This paper introduces a framework using large language models to automate metadata harmonization and integration across biomedical studies, achieving higher recovery rates than traditional methods.

This paper uses GPT-4 to automate curation of E3-substrate relationships from literature, resulting in a database of 3,294 pairs with high accuracy.

The paper develops a knowledge graph framework to extract biomedical knowledge from PubMed, creating a database that enhances transparency in biomedical AI applications.

The paper introduces a benchmark dataset (CRED) for causal relation extraction from biomedical literature, evaluates classifiers, and applies it to identify Parkinson's disease-causing genes.

This study integrates Ayurveda with modern medical data to classify rare diseases into six clusters, revealing potential ciliopathies in specific clusters.

AutoMAxO uses LLMs to semi-automate MAxO curation for rare diseases, identifying 538 confirmed disease-treatment annotations from 4,918 medical abstracts.

This paper constructs the first knowledge graph for Traditional Vietnamese Medicine (TVM) by adapting existing Traditional Chinese Medicine (TCM) resources and develops a benchmark for knowledge graph-based question-answering.

LitGene integrates textual information into gene representations using a transformer-based model with contrastive learning, improving disease risk gene prediction and interpretability.

VIOLIN reconciles heterogeneous interaction graphs with extracted molecular interactions, showing stable and interpretable results across different models and datasets.

This paper explores using natural language and ontologies to integrate genetic disease risk studies and identifies areas for improvement in data harmonization tools.

This paper uses GPT-4 to automatically annotate the severity of phenotypic abnormalities in the Human Phenotype Ontology, achieving high accuracy in clinical characteristic identification.

This paper investigates using Large Language Models for pathway curation, showing improved performance over existing methods with a GPT-3.5 fine-tuned model.

This scoping review summarizes the use of large language models in biomedical NLP tasks, highlighting their growing application in text classification, relation extraction, and named entity recognition.

The paper maps PanglaoDB cell-type markers to Wikidata to enhance reusability and FAIRness, creating structured associations and demonstrating new biological insights.

The paper uses ontologies to integrate clinical, pathological, and molecular kidney data for precision medicine analysis.

This study evaluates GPT-3.5 and GPT-4 for identifying biological processes and regulatory relationships in wound healing texts, finding GPT-4 performs better without fine-tuning.

The paper presents a human-in-the-loop system for identifying residue-level functional annotations from literature, achieving high precision, recall, and F1-measure.

The paper describes a structured curation process for the Gene2Phenotype project to systematically evaluate and organize genomic disease-gene relationships.

The paper fine-tunes BERT models to extract transcriptional regulatory interactions from bacterial literature, achieving high performance and reconstructing a significant portion of a bacterial transcriptional regulatory network.

The paper develops HSCLO38, a framework for connecting genomic features in a knowledge graph, and evaluates its performance with large-scale genomic data.

PuMA extracts and visualizes gene-celltype relations from PubMed to aid in celltype annotation.

Phenomics Assistant is a chat-based interface that uses LLMs to query the Monarch knowledge graph, improving reliability for non-expert users to explore disease-gene-phenotype relationships.

reguloGPT constructs a knowledge graph of molecular regulatory pathways using GPT-4 for entity recognition and relationship extraction, improving over existing methods and demonstrating utility in cancer research.

This paper evaluates 21 large language models for mining gene interactions and pathway knowledge, finding significant performance differences between API-based and open-source models.

The paper introduces simona, an R package for semantic similarity analysis on bio-ontologies, benchmarking methods and showing topology-based approaches are effective for less-studied organisms.

The paper introduces BioWiC, a benchmark to evaluate how well language models represent biomedical concepts in context, and establishes baselines using various large language models.