Beyond Independent Genes: Learning Module-Inductive Representations for Single-Cell Gene Perturbation Prediction
arXiv:2602.04901v2 Announce Type: replace-cross Abstract: Predicting transcriptional responses to genetic perturbations is a central problem in functional genomics. In practice, perturbation responses are rarely gene-independent but instead manifest as coordinated, program-level transcriptional changes among functionally related genes. However, most existing methods do not explicitly model such coordination, due to gene-wise modeling paradigms and reliance on static biological priors that cannot capture dynamic program reorganization. To address these limitations, we propose scBIG, a module-in
The paper directly addresses a known limitation in functional genomics, leveraging advanced AI techniques to improve perturbation prediction, indicating a maturing of AI applications in biological research.
This research enhances the ability to predict how genes interact and respond to perturbations, which is critical for drug discovery, personalized medicine, and understanding complex diseases.
The explicit modeling of gene coordination rather than gene-independent responses through scBIG offers a more accurate and comprehensive understanding of cellular biology.
- · Biotech companies
- · Pharmaceutical R&D
- · Genomics researchers
- · AI in life sciences
- · Traditional gene-wise modeling methods
- · Biological research relying on static priors
Improved accuracy in predicting gene perturbation outcomes, leading to more efficient experimental design.
Accelerated development of targeted therapies and personalized medicine approaches due to better biological understanding.
Potential for AI-driven automated drug discovery pipelines that can rapidly identify and validate therapeutic targets.
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Read at arXiv cs.LG