CausalBench
Introduced by Chevalley et al. in CausalBench: A Large-scale Benchmark for Network Inference from Single-cell Perturbation Data
CausalBench is a comprehensive benchmark suite for evaluating network inference methods on large-scale perturbational single-cell gene expression data. CausalBench introduces several biologically meaningful performance metrics and operates on two large, curated and openly available benchmark data sets for evaluating methods on the inference of gene regulatory networks from single-cell data generated under perturbations. The datasets consists of over 200000 training samples under interventions.
Papers
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