Biomedical Text Mining and Ontologies

This paper introduces txt2onto 2.0, a method for annotating biomedical metadata with disease and tissue terms using interpretable machine learning, showing improved performance over its predecessor.

NEKO is an AI workflow using LLMs to extract and organize scientific knowledge from literature, providing more specific and actionable insights than existing models.

This paper introduces a machine learning framework that extracts biological pathway information from literature images and compares it to manually curated maps.

This study evaluates seven large language models on their ability to understand genes and cells, finding they have some capability but require improvements in cell annotation tasks.

The paper develops a Lifestyle Factor Ontology and annotated corpus for detecting lifestyle factors in biomedical texts, achieving 76% F-score with a transformer-based NER system.

This paper introduces a novel corpus and language models for Named Entity Recognition of pharmacokinetic parameters in scientific literature, achieving high accuracy with BioBERT.

The paper introduces a framework that combines large language models with knowledge graphs to improve factual accuracy in biomedical question answering, demonstrating effectiveness in drug repositioning and resistance prediction.

This paper introduces a method to mine past discoveries from the biomedical literature by detecting surges in relation prevalence over time, providing a benchmark dataset for Literature-Based Discovery evaluation.

This study develops MarkerGeneBERT, an NLP system that extracts cell markers from scRNA-seq literature, achieving 76% completeness and 75% accuracy in marker gene identification.

The paper introduces a manually annotated corpus and a relation extraction model to identify physical protein interactions from biomedical literature, integrating results into the STRING database.

The paper develops a pipeline using large language models to extract food-chemical relationships from literature, identifying many novel interactions.

This paper maps natural products onto the plant tree of life using text mining and large language models, revealing lineage-specific distributions and demonstrating automation potential.

The paper improves dictionary-based named entity recognition by using a Transformer model to automatically generate block lists, increasing precision in text mining.

This paper compiles a dataset linking HRA digital objects to experts, publications, funding, and datasets to support systematic HRA construction and usage.

This study improves gene relationship prediction using iterative prompt refinement with ChatGPT, achieving better performance for complex gene interactions.

This paper fine-tunes Llama 2 for rare disease concept normalization using HPO data, achieving high accuracy with synonym inclusion and typo handling.

This paper introduces AD-Mapper, a tool for semantic harmonization of Alzheimer's disease datasets, which outperforms a string-matching baseline.

This paper develops an annotation pipeline and two deep learning models for enzyme named entity recognition, achieving high performance with SciBERT-BiLSTM outperforming BioBERT-BiLSTM.

The paper develops a web portal and NLP pipeline to extract and visualize gene-specific Alzheimer's disease publications from PubMed.

The paper introduces pathCLIP, a system that identifies genes and gene relations from biological pathway figures using image-text contrastive learning, outperforming single-modality approaches.

GO2Sum generates human-readable protein function summaries from GO terms using a fine-tuned T5 model, outperforming the base T5 model in creating function descriptions for UniProt entries.

The paper updates the Arabidopsis Information Resource, detailing its curation efforts, infrastructure improvements, and analysis of gene function annotations.

preon normalizes drug names and cancer types in large-scale data for precision oncology.

The paper introduces CoNECo, a corpus for NER and NEN of protein complexes, achieving F1-scores of 73.7% and 61.2% with transformer-based and dictionary-based taggers, respectively.

The paper develops an ensemble transformer model using gene and chemical descriptions to extract drug-target interactions from biomedical literature, achieving high performance on a benchmark dataset.

The paper integrates the Medpipe microservice with Pannotator to enhance genomic annotation with immunological and subcellular location features.

RegulaTome provides a comprehensive corpus of biomedical relations, enabling improved relation extraction with a Transformer model achieving 66.6% F1-score.

The paper extends the Protein Kinase Ontology (ProKinO) to uncover new insights about dark kinases, identifying PAK5 as a frequently mutated kinase in cancers and highlighting its potential in drug discovery.

The paper develops CeN-CAM, a semantic model based on GO-CAM, to represent causal relationships in C. elegans neural circuits and behaviors using literature data.

SKiM discovers and annotates biomedical knowledge using co-occurrence and transformer models to find A-B-C relationships between user-defined concepts.