Back

DELPHAI predicts heterogeneous perturbation responses with learned cell fitness and gene-space retrieval

Zhang, X.; Wu, H.; Liu, H.

2026-07-06 bioinformatics
10.64898/2026.07.01.735965 bioRxiv
Show abstract

Modelling heterogeneous cellular responses to perturbation holds the promise of scalable in silico screening and mechanistic insight. However, mass conservation despite cell-type-specific depletion, and lossy projections from gene space to latent space, hinder performance of state-of-the-art methods. DELPHAI, with learned per-cell-fitness filtering out depleted cells and gene-space retrieval bypassing the latent bottleneck, outperforms all baseline methods across two benchmark frameworks and offers explainability with inferred cell-type-specific survival without any biological priors.

Matching journals

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

1
Nature Communications
5641 papers in training set
Top 6%
22.1%
2
Cell Systems
201 papers in training set
Top 0.1%
18.6%
3
Genome Biology
637 papers in training set
Top 2%
6.8%
4
Molecular Systems Biology
162 papers in training set
Top 0.3%
5.5%
50% of probability mass above
5
PLOS Computational Biology
1863 papers in training set
Top 9%
3.5%
6
eLife
5828 papers in training set
Top 38%
2.7%
7
Proceedings of the National Academy of Sciences
2444 papers in training set
Top 21%
2.5%
8
Nature
645 papers in training set
Top 5%
2.5%
9
Nature Methods
385 papers in training set
Top 3%
2.4%
10
Briefings in Bioinformatics
354 papers in training set
Top 3%
2.4%
11
Nature Machine Intelligence
70 papers in training set
Top 1%
1.7%
12
Nature Genetics
286 papers in training set
Top 3%
1.7%
13
Bioinformatics
1204 papers in training set
Top 7%
1.5%
14
Genome Research
468 papers in training set
Top 5%
1.1%
15
npj Systems Biology and Applications
125 papers in training set
Top 1%
1.1%
16
Nucleic Acids Research
1281 papers in training set
Top 11%
1.1%
17
Science Advances
1243 papers in training set
Top 24%
1.1%
18
Nature Biotechnology
172 papers in training set
Top 3%
1.1%
19
Science
477 papers in training set
Top 7%
1.1%
20
Nature Cell Biology
118 papers in training set
Top 3%
1.0%
21
PRX Life
42 papers in training set
Top 0.8%
1.0%
22
iScience
1154 papers in training set
Top 32%
0.9%
23
Advanced Science
286 papers in training set
Top 9%
0.8%
24
Scientific Reports
3612 papers in training set
Top 73%
0.8%
25
Cell Reports
1498 papers in training set
Top 27%
0.8%
26
Communications Biology
993 papers in training set
Top 29%
0.8%
27
Cell Reports Methods
165 papers in training set
Top 4%
0.6%
28
New Phytologist
346 papers in training set
Top 5%
0.6%