mlsanityData to 14 Oct 2026

LLM4GRN: Discovering Causal Gene Regulatory Networks with LLMs -- Evaluation through Synthetic Data Generation

arXiv 2410.15828 ↗First posted 21 Oct 2024cs.AIcited by 7

On the map: Synthetic data and self-improvement for LLMs → LLM synthetic data generation

Abstract (arXiv)

Gene regulatory networks (GRNs) represent the causal relationships between transcription factors (TFs) and target genes in single-cell RNA sequencing (scRNA-seq) data. Understanding these networks is crucial for uncovering disease mechanisms and identifying therapeutic targets. In this work, we investigate the potential of large language models (LLMs) for GRN discovery, leveraging their learned biological knowledge alone or in combination with traditional statistical methods. We develop a task-based evaluation strategy to address the challenge of unavailable ground truth causal graphs. Specifically, we use the GRNs suggested by LLMs to guide causal synthetic data generation and compare the resulting data against the original dataset. Our statistical and biological assessments show that LLMs can support statistical modeling and data synthesis for biological research.

Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). Placed in LLM synthetic data generation, one of 13 directions in Synthetic data and self-improvement for LLMs. Builds on 28 of its 64 references that are in the map. First posted on arXiv 21 Oct 2024.

Builds on

28 of 64 references.

References · 28

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2022Survey of Hallucination in Natural Language Generationcited by 2,075
  4. 2024The AI Scientist: Towards Fully Automated Open-Ended Scientific Discoverycited by 1,071
  5. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 752
  6. 2023Causal Reasoning and Large Language Models: Opening a New Frontier for Causalitycited by 353
  7. 2023Effective Long-Context Scaling of Foundation Modelscited by 303
  8. 2020Differentiable Causal Discovery from Interventional Datacited by 218
  9. 2017CausalGAN: Learning Causal Implicit Generative Models with Adversarial Trainingcited by 152
  10. 2023The Impact of Large Language Models on Scientific Discovery: a Preliminary Study using GPT-4cited by 112
  11. 2021DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networkscited by 109
  12. 2021Efficient Neural Causal Discovery without Acyclicity Constraintscited by 82
16 more
  1. 2024Scientific Large Language Models: A Survey on Biological & Chemical Domainscited by 80
  2. 2023Integrating Large Language Model for Improved Causal Discoverycited by 54
  3. 2024Efficient Causal Graph Discovery Using Large Language Modelscited by 53
  4. 2023Causal Order: The Key to Leveraging Imperfect Experts in Causal Inferencecited by 48
  5. 2024A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Modelscited by 45
  6. 2024Causal Inference with Large Language Model: A Surveycited by 32
  7. 2023Comparing Llama-2 and GPT-3 LLMs for HPC kernels generationcited by 30
  8. 2024ALCM: Autonomous LLM-Augmented Causal Discovery Frameworkcited by 29
  9. 2024Large Language Models for Causal Discovery: Current Landscape and Future Directionscited by 25
  10. 2023Causal Structure Learning Supervised by Large Language Modelcited by 21
  11. 2021Causal-TGAN: Generating Tabular Data Using Causal Generative Adversarial Networkscited by 12
  12. 2024ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Predictioncited by 12
  13. 2024Evaluating Interventional Reasoning Capabilities of Large Language Modelscited by 6
  14. 2024Context-Aware Reasoning On Parametric Knowledge for Inferring Causal Variablescited by 6
  15. 2022Domain Knowledge in A*-Based Causal Discoverycited by 4
  16. 2023Boosting Synthetic Data Generation with Effective Nonlinear Causal Discoverycited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 5.

7 papers.

  1. 2025A Survey of AI Scientistscited by 11
  2. 2026scPilot: Large Language Model Reasoning Toward Automated Single-Cell Analysis and Discoverycited by 4
  3. 2025Causality Is Key to Understand and Balance Multiple Goals in Trustworthy ML and Foundation Modelscited by 2
  4. 2026MechPert: Mechanistic Consensus as an Inductive Bias for Unseen Perturbation Predictioncited by 1
  5. 2026Trustworthy AI Suffers from Invariance Conflicts and Causality is The Solutioncited by 1
  6. 2026DN-Hypo-Pipeline: An AI-Driven Workflow for Generating Hypotheses using Large Language Models and Scientific Explanationscited by 0
  7. 2026Evaluating Agentic Bioinformatics through Function, Evidence, and Validationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024CausalGraph2LLM: Evaluating LLMs for Causal Queries
  2. 2026CoDiffGRN: Rethinking Gene Regulatory Network Inference via the BEELINE-KGC Benchmark and Co-evolutionary Discrete Diffusion
  3. 2024LLM-initialized Differentiable Causal Discovery
  4. 2025Beyond Correlation: Towards Causal Large Language Model Agents in Biomedicine
  5. 2026Causal dictionary learning reveals and validates transcription-factor binding features in genomic language models
  6. 2025Scientific Hypothesis Generation and Validation: Methods, Datasets, and Future Directions
  7. 2026LLM Explainability with Counterfactual Chains and Causal Graphs
  8. 2024Causal Graph Discovery with Retrieval-Augmented Generation based Large Language Models