Back

ImmuneFM: Pre-training Foundation Model from Cytometry Data for Immunology Research

Ding, S.; Bhattacharya, S.; Butte, A.

2025-07-11 immunology
10.1101/2025.07.09.664020 bioRxiv
Show abstract

Immunology is an essential field in the biomedicine domain, which plays an important role in oncology, vaccines, infection, etc. With the increasing amount of data available in immunology and artificial intelligence technique development, there is a need to develop data-driven AI methods in the field. However, the data from various immunology studies is very hard to integrate into an AI-ready dataset due to the lack of a standard. Moreover, independent immunology studies data lacks enough labels to train the supervised model. Motivated by these challenges, we curated a large-scale AI-ready cytometry dataset for immunology from the publicly available ImmPort portal. We design the framework to pre-train a foundation model, ImmuneFM, on the cytometry dataset. ImmuneFM can be applied to a wide range of downstream immunology diseases with fine-tuning on a limited number of labeled samples. The experiment results on eight downstream tasks demonstrate the superior performance of ImmuneFM compared to baseline deep learning and traditional methods.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

1
Frontiers in Immunology
○ 638 papers in training set
Top 0.4%
18.7%
Similar papers in this journal
2
Scientific Reports
○ 3612 papers in training set
Top 2%
13.0%
Similar papers in this journal
3
Computers in Biology and Medicine
○ 128 papers in training set
Top 0.6%
5.5%
Similar papers in this journal
4
iScience
○ 1154 papers in training set
Top 4%
4.1%
Similar papers in this journal
5
Patterns
○ 78 papers in training set
Top 0.4%
4.1%
6
npj Systems Biology and Applications
○ 125 papers in training set
Top 0.4%
4.1%
Similar papers in this journal
7
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 1%
3.3%
50% of probability mass above
8
IEEE/ACM Transactions on Computational Biology and Bioinformatics
● 38 papers in training set
Top 0.3%
3.3%
9
PLOS Computational Biology
● 1863 papers in training set
Top 10%
3.3%
10
Heliyon
○ 152 papers in training set
Top 2%
2.8%
11
Communications Medicine
○ 113 papers in training set
Top 0.9%
2.5%
12
Vaccines
○ 198 papers in training set
Top 2%
1.7%
13
Communications Biology
○ 993 papers in training set
Top 13%
1.7%
14
Nature Communications
○ 5641 papers in training set
Top 47%
1.5%
15
npj Precision Oncology
○ 53 papers in training set
Top 1%
1.1%
16
Journal of Translational Medicine
○ 57 papers in training set
Top 1%
1.1%
17
Life
○ 29 papers in training set
Top 0.4%
1.1%
18
PLOS ONE
● 5266 papers in training set
Top 55%
1.0%
19
Frontiers in Bioinformatics
○ 49 papers in training set
Top 1%
0.9%
20
eLife
● 5828 papers in training set
Top 66%
0.9%
21
Briefings in Bioinformatics
◐ 354 papers in training set
Top 7%
0.9%
22
Scientific Data
○ 209 papers in training set
Top 3%
0.6%
23
BMC Medical Genomics
○ 50 papers in training set
Top 1%
0.6%
24
IEEE Journal of Biomedical and Health Informatics
● 37 papers in training set
Top 1%
0.6%
25
Bioinformatics Advances
◐ 203 papers in training set
Top 5%
0.6%
26
Lab on a Chip
● 96 papers in training set
Top 1%
0.6%
27
Bioinformatics
◐ 1204 papers in training set
Top 9%
0.6%
28
Cells
○ 249 papers in training set
Top 8%
0.6%
29
International Journal of Molecular Sciences
○ 494 papers in training set
Top 17%
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.