A large-scale benchmark for network inference from single-cell perturbation data.

ABSTRACT: Mapping biological mechanisms in cellular systems is a fundamental step in early-stage drug discovery that serves to generate hypotheses on what disease-relevant molecular targets may effectively be modulated by pharmacological interventions. With the advent of high-throughput methods for measuring single-cell gene expression under genetic perturbations, we now have effective means for generating evidence for causal gene-gene interactions at scale. However, evaluating the performance of network inference methods in real-world environments is challenging due to the lack of ground-truth knowledge. Moreover, traditional evaluations conducted on synthetic datasets do not reflect the performance in real-world systems. We thus introduce CausalBench, a benchmark suite revolutionizing network infe

SUBMITTER: Chevalley M 

PROVIDER: S-EPMC11897147 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature