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Cell phenotypes in the biomedical literature: a systematic analysis and text mining corpus

Rotenberg, N. H.; Leaman, R.; Islamaj, R.; Kuivaniemi, H.; Tromp, G.; Fluharty, B.; Richardson, S.; Eastwood, C.; Diller, M.; Xu, B.; Pankajam, A. V.; Osumi-Sutherland, D.; Lu, Z.; Scheuermann, R. H.

2026-02-14 bioinformatics
10.64898/2026.02.11.705457 bioRxiv
Show abstract

The variety of cell phenotypes identified by single-cell technologies is rapidly expanding, yet this knowledge is dispersed across the scientific literature and incompletely represented in structured resources. We present the CellLink corpus, a manually annotated collection of over 22,000 mentions of human and mouse cell populations in recent journal articles, distinguishing specific cell phenotypes, heterogeneous cell populations, and vague cell populations, and linking to Cell Ontology (CL) terms as either exact or related matches, covering nearly half of the terms in the current CL. A systematic analysis reveals lineage-specific patterns in how authors utilize anatomical context, molecular signatures, functional roles, developmental stage, and other attributes in cell naming. We show that fine-tuning transformer-based models on CellLink yields strong performance for named entity recognition, while embedding-based approaches support zero-shot entity linking and distinguishing exact from related matches. We further demonstrate the utility of CellLink to expand and refine the chondrocyte branch of CL.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Scientific Data
○ 209 papers in training set
Top 0.1%
34.8%
Similar papers in this journal
2
Genome Biology
○ 637 papers in training set
Top 2%
7.4%
Similar papers in this journal
3
Nature Communications
○ 5641 papers in training set
Top 28%
5.2%
Similar papers in this journal
4
Nature Methods
○ 385 papers in training set
Top 2%
4.9%
Similar papers in this journal
50% of probability mass above
5
Bioinformatics
◐ 1204 papers in training set
Top 5%
3.3%
Similar papers in this journal
6
Nature
○ 645 papers in training set
Top 5%
2.8%
7
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 2%
2.4%
8
Scientific Reports
○ 3612 papers in training set
Top 41%
2.4%
9
Nucleic Acids Research
◐ 1281 papers in training set
Top 7%
2.4%
10
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 3%
1.8%
11
BMC Biology
○ 265 papers in training set
Top 2%
1.8%
12
Database
◐ 61 papers in training set
Top 0.4%
1.8%
13
Nature Biotechnology
○ 172 papers in training set
Top 2%
1.7%
14
eLife
● 5828 papers in training set
Top 53%
1.5%
15
Life Science Alliance
● 285 papers in training set
Top 4%
1.5%
16
iScience
○ 1154 papers in training set
Top 23%
1.4%
17
GigaScience
◐ 212 papers in training set
Top 3%
1.4%
18
PLOS ONE
● 5266 papers in training set
Top 50%
1.4%
19
Briefings in Bioinformatics
◐ 354 papers in training set
Top 6%
1.1%
20
Stem Cell Reports
○ 130 papers in training set
Top 2%
0.9%
21
Cell Systems
○ 201 papers in training set
Top 4%
0.9%
22
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 40%
0.9%
23
Molecular Systems Biology
○ 162 papers in training set
Top 3%
0.9%
24
Frontiers in Bioinformatics
○ 49 papers in training set
Top 2%
0.6%
25
Science
● 477 papers in training set
Top 10%
0.6%
26
Patterns
○ 78 papers in training set
Top 3%
0.6%
27
Cell Genomics
○ 172 papers in training set
Top 5%
0.6%
28
The American Journal of Human Genetics
○ 234 papers in training set
Top 3%
0.6%
29
Genetics in Medicine
○ 78 papers in training set
Top 1.0%
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.