PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis
<|reference_start|>PerturBench: Benchmarking Machine Learning Models for Cellular Perturbation Analysis: We present a comprehensive framework for predicting the effects of perturbations in single cells, designed to standardize benchmarking in this rapidly evolving field. Our framework, PerturBench, includes a user-friendly platform, diverse datasets, metrics for fair model comparison, and detailed performance analysis. Extensive evaluations of published and baseline models reveal limitations like mode or posterior collapse, and underscore the importance of rank metrics that assess the ordering of perturbations alongside traditional measures like RMSE. Our findings show that simple models can outperform more complex approaches. This benchmarking exercise sets new standards for model evaluation, supports robust model development, and advances the potential of these models to use high-throughput and high-content genetic and chemical screens for disease target discovery.<|reference_end|>
@article{wu2024perturbench:,
    title={PerturBench: Benchmarking Machine Learning Models for Cellular
  Perturbation Analysis},
    author={Yan Wu, Esther Wershof, Sebastian M Schmon, Marcel Nassar, B{l}a.zej
  Osi'nski, Ridvan Eksi, Kun Zhang, Thore Graepel},
    journal={arXiv preprint arXiv:2408.10609},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10609},
    primaryClass={cs.LG q-bio.GN stat.ML}
}