---
title: "Automating Exploratory Proteomics Research via Language"
description: "The authors present PROTEUS, an LLM-driven system that starts from raw protein expression data… 85 references, cited by 6 papers here."
authors: ["Ning Ding", "Shang Qu", "Linhai Xie", "Yifei Li", "Zaoqu Liu", "Kaiyan Zhang", "Yibai Xiong", "Yuxin Zuo", "Zhangren Chen", "Ermo Hua", "Xingtai Lv", "Youbang Sun", "Yang Li", "Dong Li", "Fuchu He", "Bowen Zhou"]
year: 2024
venue: "arXiv:2411.03743"
source: "https://arxiv.org/abs/2411.03743"
doi: "10.48550/arxiv.2411.03743"
arxiv: "2411.03743"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/ding-2024-automating-exploratory-proteomics-research-language/"
---
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# Automating Exploratory Proteomics Research via Language Models

**Authors:** [Ning Ding](https://md.research.elamur.ai/free/en/authors/ning-ding/), [Shang Qu](https://md.research.elamur.ai/free/en/authors/shang-qu/), Linhai Xie, [Yifei Li](https://md.research.elamur.ai/free/en/authors/yifei-li/), Zaoqu Liu, [Kaiyan Zhang](https://md.research.elamur.ai/free/en/authors/kaiyan-zhang/), Yibai Xiong, [Yuxin Zuo](https://md.research.elamur.ai/free/en/authors/yuxin-zuo/), [Zhangren Chen](https://md.research.elamur.ai/free/en/authors/zhangren-chen/), [Ermo Hua](https://md.research.elamur.ai/free/en/authors/ermo-hua/), Xingtai Lv, [Youbang Sun](https://md.research.elamur.ai/free/en/authors/youbang-sun/), [Yang Li](https://md.research.elamur.ai/free/en/authors/yang-li/), [Dong Li](https://md.research.elamur.ai/free/en/authors/dong-li/), Fuchu He and [Bowen Zhou](https://md.research.elamur.ai/free/en/authors/bowen-zhou/) · **Year:** 2024 · **Venue:** arXiv:2411.03743 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2411.03743) · [PDF](https://arxiv.org/pdf/2411.03743) · [DOI 10.48550/arxiv.2411.03743](https://doi.org/10.48550/arxiv.2411.03743) · [arXiv 2411.03743](https://arxiv.org/abs/2411.03743) · [OpenAlex](https://openalex.org/W4404364191)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [6](https://md.research.elamur.ai/free/en/papers/ding-2024-automating-exploratory-proteomics-research-language/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Proteomics studies produce large, complex datasets, but turning them into biological insight still depends on experts who choose analyses by hand. That process is slow and can be shaped by a researcher's personal habits and blind spots. The authors present PROTEUS, an LLM-driven system that starts from raw protein expression data plus sample metadata and ends with a list of scientific hypotheses, with no human involvement. Its planning works on three tiers: research objectives, analysis workflows and individual steps. The system runs specialized bioinformatics tools, interprets their outputs and revises later workflows and objectives after each round. The core model is Llama 3.1 70B, further fine-tuned on the UltraMedical biomedical instruction set; an open model was chosen partly because proteomics data are privacy-sensitive. On 12 datasets covering single-cell and bulk samples from sources such as tumors and immune cells, PROTEUS generated 191 hypotheses. These were judged by LLM-based scoring and by human experts. The results suggest the outputs are coherent, consistent with prior literature and partly novel, pointing toward end-to-end automated discovery in omics.

## Key points
- PROTEUS (PROTeomics Exploration and Understanding System) uses three planning levels (objectives → workflows → steps). Planner, Updater, Parameter Selector and Result Analyzer modules refine the analysis iteratively.
- The backbone is Llama 3.1 70B, fine-tuned on the UltraMedical dataset. The system calls tools such as CATALYST, diffcyt and BioEnricher.
- The evaluation covered 12 proteomics datasets (single-cell and bulk; immune cells, tumors, etc.) and produced 191 hypotheses in total.
- Automatic scoring used LLM judges on 5 metrics, each rated 0–5. The metrics include literature-based support, literature-based novelty, logical coherence and evaluability, with access to PubMed articles and the original paper.
- Human experts scored a random subset with the same rubric and gave open-ended feedback, which served to validate the automatic scores. The authors contrast this with prior systems such as DREAM, which only checked whether the initial question was resolved.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

📚 Shelves (6): [🎯 Decision Sciences](https://md.research.elamur.ai/free/en/shelves/decision-sciences/) · 306 works · [🧬 Biochemistry, Genetics and Molecular Biology](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/) · 273 works · [🧬 Molecular Biology](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/molecular-biology/) · 270 works · [🎯 Information Systems and Management](https://md.research.elamur.ai/free/en/shelves/decision-sciences/information-systems-and-management/) · 96 works · [🧪 Chemistry](https://md.research.elamur.ai/free/en/shelves/chemistry/) · 6 works · [🧪 Spectroscopy](https://md.research.elamur.ai/free/en/shelves/chemistry/spectroscopy/) · 1 work

🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 4,145 works

## Cite

Cite as: Ning Ding, Shang Qu, Linhai Xie, Yifei Li, Zaoqu Liu, Kaiyan Zhang, Yibai Xiong, Yuxin Zuo, Zhangren Chen, Ermo Hua, Xingtai Lv, Youbang Sun, Yang Li, Dong Li, Fuchu He and Bowen Zhou. 2024. Automating Exploratory Proteomics Research via Language Models. arXiv:2411.03743.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{ding2024automating,
  title = {{Automating Exploratory Proteomics Research via Language Models}},
  author = {Ning Ding, Shang Qu, Linhai Xie, Yifei Li, Zaoqu Liu, Kaiyan Zhang, Yibai Xiong, Yuxin Zuo, Zhangren Chen, Ermo Hua, Xingtai Lv, Youbang Sun, Yang Li, Dong Li, Fuchu He and Bowen Zhou},
  year = {2024},
  doi = {10.48550/arxiv.2411.03743},
  eprint = {2411.03743},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2411.03743}
}
```

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*Updated 3 November 2026*