scBalFlow: A Staged Flow Matching Framework for Imbalanced Single-Cell Drug Perturbation Prediction

Clicks: 18
ID: 328834
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #612 of 983 articles by views in BMC Bioinformatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 983 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
MOTIVATION: Conventional drug perturbation prediction models typically employ end-to-end encoder-decoder architectures, directly mapping control samples and perturbation conditions to post-perturbation gene expression profiles. However, these approaches widely overlook the severe class imbalance inherent in perturbation datasets, leading to a predictive bias toward weakly responsive samples. RESULTS: To address this bottleneck, we propose scBalFlow, a decoupled two-stage training framework. The first stage predicts the perturbation response intensity under given conditions, employing a Gaussian-Augmented Inference (GAI) strategy to counteract data imbalance. Crucially, the second stage bypasses weakly responsive conditions, while utilizing a Flow Matching model to synthesize highly responsive samples. Comprehensive evaluations on large-scale benchmarks, including SciPlex3 and McFarland, demonstrate that scBalFlow effectively overcomes the imbalance issue and significantly outperforms existing state-of-the-art methods on imbalanced datasets, particularly in capturing complex distribution shifts and maintaining single-cell distributional consistency. AVAILABILITY: The source code and datasets are available at GitHub https://github.com/hanwenlv-cmd/scBalFlow and Figshare with DOI: 10.6084/m9.figshare.33137447. The datasets of SciPlex3, ComboSciPlex, and McFarland underlying this study are available via the pertpy package. Alternatively, they can be downloaded manually from https://exampledata.scverse.org/pertpy/srivatsan_2020_sciplex3.h5ad for SciPlex3, https://exampledata.scverse.org/pertpy/combosciplex.h5ad for combosciplex, and https://exampledata.scverse.org/pertpy/mcfarland_2020.h5ad for McFarland. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7213361319 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hanwen Lyu, Jiawei Luo
Are you one of these authors? Claim your profile
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag682
URL
https://doi.org/10.1093/bioinformatics/btag682
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.