ArticleBriefings in bioinformatics2024
A large language model framework for literature-based disease-gene association prediction.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Benchmarking commercial large language models for gene-disease-phenotype extraction from full-text human genetics literature.Quantitative biology (Beijing, China) · 2026Article
- Large language models for pneumonia detection in radiology reports via text analysis.PLOS digital health · 2026Article
- Article
- LLMs uncover 6M1@C60's therapeutic targets: inhibiting atherosclerotic progression via NRF2/HO-1/GPX4 axis-mediated ferroptosis suppression.Journal of nanobiotechnology · 2026Article
- Genomics of drug target prioritization for complex diseases.Nature reviews. Genetics · 2026Review
- BioTriplex: a full-text annotated corpus for fine-tuning language models in gene-disease relation extraction tasks.Bioinformatics (Oxford, England) · 2026Article
- Large Language Model-Enhanced Drug Repositioning Knowledge Extraction via Long Chain-of-Thought: Development and Evaluation Study.JMIR medical informatics · 2025Article
- Evaluation of large language models in patient education and clinical decision support for rotator cuff injury: a two-phase benchmarking study.BMC medical informatics and decision making · 2025Article
- Multiple Confabulations Found in Bioinformatics Tasks Carried Out by Several Free Large Language Models.Current genomics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
With the exponential growth of biomedical literature, leveraging Large Language Models (LLMs) for automated medical knowledge understanding has become increasingly critical for advancing precision medicine. However, current approaches face significant challenges in reliability, verifiability, and scalability when extracting complex biological relationships from scientific literature using LLMs. To overcome the obstacles of LLM development in biomedical literature understating, we propose LORE, a novel unsupervised two-stage reading methodology with LLM that models literature as a knowledge graph of verifiable factual statements and, in turn, as semantic embeddings in Euclidean space. LORE captured essential gene pathogenicity information when applied to PubMed abstracts for large-scale understanding of disease-gene relationships. We demonstrated that modeling a latent pathogenic flow in the semantic embedding with supervision from the ClinVar database led to a 90% mean average precision in identifying relevant genes across 2097 diseases. This work provides a scalable and reproducible approach for leveraging LLMs in biomedical literature analysis, offering new opportunities for researchers to identify therapeutic targets efficiently.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.