# Gene Set Summarization using Large Language Models

arXiv: https://arxiv.org/abs/2305.13338 · HTML: https://mlsanity.com/paper/2305.13338/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 46 references that are in the map.
- First posted on arXiv 21 May 2023.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Emergent Abilities of Large Language Models](https://mlsanity.com/paper/2206.07682/) (2022)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [GeneGPT: Augmenting Large Language Models with Domain Tools for Improved Access to Biomedical Information](https://mlsanity.com/paper/2304.09667/) (2023)
- [Eight Things to Know about Large Language Models](https://mlsanity.com/paper/2304.00612/) (2023)
- [Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning](https://mlsanity.com/paper/2304.02711/) (2023)

## Built on by (most cited first, 12 of 12)

- [AI Hallucinations: A Misnomer Worth Clarifying](https://mlsanity.com/paper/2401.06796/) (2024)
- [UPS: Efficiently Building Foundation Models for PDE Solving via Cross-Modal Adaptation](https://mlsanity.com/paper/2403.07187/) (2024)
- [Evaluation of large language models for discovery of gene set function](https://mlsanity.com/paper/2309.04019/) (2023)
- [Large language models improve Alzheimer's disease diagnosis using multi-modality data](https://mlsanity.com/paper/2305.19280/) (2023)
- [GeneAgent: Self-verification Language Agent for Gene Set Knowledge Discovery using Domain Databases](https://mlsanity.com/paper/2405.16205/) (2024)
- [LitSumm: Large language models for literature summarisation of non-coding RNAs](https://mlsanity.com/paper/2311.03056/) (2023)
- [CurateGPT: A flexible language-model assisted biocuration tool](https://mlsanity.com/paper/2411.00046/) (2024)
- [Gene-R1: Reasoning with Data-Augmented Lightweight LLMs for Gene Set Analysis](https://mlsanity.com/paper/2509.10575/) (2025)
- [GPTON: Generative Pre-trained Transformers enhanced with Ontology Narration for accurate annotation of biological data](https://mlsanity.com/paper/2410.10899/) (2024)
- [Chemical classification program synthesis using generative artificial intelligence](https://mlsanity.com/paper/2505.18470/) (2025)
- [Discrimination by LLMs: Cross-lingual Bias Assessment and Mitigation in Decision-Making and Summarisation](https://mlsanity.com/paper/2509.09735/) (2025)
- [Leveraging LLMs for Collaborative Ontology Engineering in Parkinson Disease Monitoring and Alerting](https://mlsanity.com/paper/2512.14288/) (2025)

## Abstract (arXiv)

Molecular biologists frequently interpret gene lists derived from high-throughput experiments and computational analysis. This is typically done as a statistical enrichment analysis that measures the over- or under-representation of biological function terms associated with genes or their properties, based on curated assertions from a knowledge base (KB) such as the Gene Ontology (GO). Interpreting gene lists can also be framed as a textual summarization task, enabling the use of Large Language Models (LLMs), potentially utilizing scientific texts directly and avoiding reliance on a KB. We developed SPINDOCTOR (Structured Prompt Interpolation of Natural Language Descriptions of Controlled Terms for Ontology Reporting), a method that uses GPT models to perform gene set function summarization as a complement to standard enrichment analysis. This method can use different sources of gene functional information: (1) structured text derived from curated ontological KB annotations, (2) ontology-free narrative gene summaries, or (3) direct model retrieval. We demonstrate that these methods are able to generate plausible and biologically valid summary GO term lists for gene sets. However, GPT-based approaches are unable to deliver reliable scores or p-values and often return terms that are not statistically significant. Crucially, these methods were rarely able to recapitulate the most precise and informative term from standard enrichment, likely due to an inability to generalize and reason using an ontology. Results are highly nondeterministic, with minor variations in prompt resulting in radically different term lists. Our results show that at this point, LLM-based methods are unsuitable as a replacement for standard term enrichment analysis and that manual curation of ontological assertions remains necessary.