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OmniCellAgent: Towards AI Co-Scientists for Scientific Discovery in Precision Medicine

Huang, D.; Li, H.; Li, W.; Zhang, H.; Dickson, P.; Zhan, M.; Miller, J. P.; Cruchaga, C.; Province, M.; Chen, Y.; Payne, P.; Li, F.

2025-08-04 bioinformatics
10.1101/2025.07.31.667797 bioRxiv
Show abstract

The convergence of large language models (LLMs), AI agents, and large-scale omic datasets--such as single-cell omics, marks the arrival of a critical inflection point in biomedical research, via autonomous data mining and novel hypothesis generation. However, there is no specifically designed agentic AI model that can systematically integrate large-scale single-cell (sc) RNAseq (covering diverse diseases and cell types), omic data analytic tools, accumulated biomedical knowledge, and literature search to facilitate autonomous scientific discovery in precision medicine. In this study, we develop a novel agentic AI, OmniCellAgent, to empower non-computational-expert users--such as patients and family members, clinicians, and wet-lab researchers--to conduct scRNA-seq data-driven biomedical research like experts, uncovering molecular disease mechanisms and identifying effective precision therapies. The code of omniCellAgent is publicly accessible at: https://fuhailiailab.github.io/.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

1
Bioinformatics
◐ 1204 papers in training set
Top 0.3%
39.9%
Similar papers in this journal
2
Bioinformatics Advances
◐ 203 papers in training set
Top 0.1%
15.2%
Similar papers in this journal
50% of probability mass above
3
Patterns
○ 78 papers in training set
Top 0.2%
5.5%
Similar papers in this journal
4
GigaScience
◐ 212 papers in training set
Top 1%
3.5%
Similar papers in this journal
5
Nucleic Acids Research
◐ 1281 papers in training set
Top 7%
2.5%
Similar papers in this journal
6
Briefings in Bioinformatics
◐ 354 papers in training set
Top 4%
2.1%
7
Database
◐ 61 papers in training set
Top 0.3%
2.1%
8
Genome Biology
○ 637 papers in training set
Top 5%
2.1%
9
BMC Bioinformatics
○ 457 papers in training set
Top 4%
1.7%
10
PLOS Computational Biology
● 1863 papers in training set
Top 14%
1.7%
11
iScience
○ 1154 papers in training set
Top 17%
1.7%
12
Nature Communications
○ 5641 papers in training set
Top 47%
1.4%
13
Cell Systems
○ 201 papers in training set
Top 3%
1.1%
14
eLife
● 5828 papers in training set
Top 58%
1.1%
15
Genome Research
● 468 papers in training set
Top 5%
1.1%
16
Nature Methods
○ 385 papers in training set
Top 6%
1.0%
17
Frontiers in Genetics
○ 230 papers in training set
Top 5%
0.9%
18
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 8%
0.6%
19
Journal of Molecular Biology
○ 232 papers in training set
Top 4%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.