ArticleBioinformatics (Oxford, England)2025
ESCARGOT: an AI agent leveraging large language models, dynamic graph of thoughts, and biomedical knowledge graphs for enhanced reasoning.
Article in Bioinformatics (Oxford, England), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Computational paradigms for antimicrobial resistance prediction: integrating multi-omics, structural modeling, and foundation artificial intelligence systems.Briefings in bioinformatics · 2026Pooled it
- Agentic systems in computational pathology: architectures, evidence, and translational challenges.Journal of translational medicine · 2026Review
- A comprehensive survey of AI agents in healthcare.Journal of biomedical informatics · 2026Review
- scHilda: Hierarchical Integration of LLM with KG database for single cell type annotation.PLoS computational biology · 2026Article
- Agentic AI and the rise of in silico team science in biomedical research.Nature biotechnology · 2026Review
- Bridging data and discovery: a survey on knowledge graphs in AI for science.National science review · 2026Review
- Fine-tuning LLM hyperparameters to align semantic and physiological contexts of aging-related pathways.Molecular diversity · 2026Article
- Article
- CellDuality: Unlocking Biological Reasoning in LLMs with Self-Supervised RLVR.... International Conference on Learning Representations · 2026Article
- Improving Large Language Model Applications in the Medical and Nursing Domains With Retrieval-Augmented Generation: Scoping Review.Journal of medical Internet research · 2025Article
- Evaluating GPT-4's visual interpretation and clinical reasoning on emergency settings: A 5-year analysis.Journal of the Chinese Medical Association : JCMA · 2025Observational
- Drug repurposing for Alzheimer's disease using a graph-of-thoughts based large language model to infer drug-disease relationships in a comprehensive knowledge graph.BioData mining · 2025Article
- Automatic biomarker discovery and enrichment with BRAD.Bioinformatics (Oxford, England) · 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
9 authors.
Funding
Abstract
motivationLLMs like GPT-4, despite their advancements, often produce hallucinations and struggle with integrating external knowledge effectively. While Retrieval-Augmented Generation (RAG) attempts to address this by incorporating external information, it faces significant challenges such as context length limitations and imprecise vector similarity search. ESCARGOT aims to overcome these issues by combining LLMs with a dynamic Graph of Thoughts and biomedical knowledge graphs, improving output reliability, and reducing hallucinations.
resultESCARGOT significantly outperforms industry-standard RAG methods, particularly in open-ended questions that demand high precision. ESCARGOT also offers greater transparency in its reasoning process, allowing for the vetting of both code and knowledge requests, in contrast to the black-box nature of LLM-only or RAG-based approaches. AVAILABILITY AND IMPLEMENTATION: ESCARGOT is available as a pip package and on GitHub at: https://github.com/EpistasisLab/ESCARGOT.
Indexed as
Identifiers
What Socratic holds
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.