CMOB: Large-Scale Cancer Multi-Omics Benchmark with Open Datasets, Tasks, and Baselines
<|reference_start|>CMOB: Large-Scale Cancer Multi-Omics Benchmark with Open Datasets, Tasks, and Baselines: Machine learning has shown great potential in the field of cancer multi-omics studies, offering incredible opportunities for advancing precision medicine. However, the challenges associated with dataset curation and task formulation pose significant hurdles, especially for researchers lacking a biomedical background. Here, we introduce the CMOB, the first large-scale cancer multi-omics benchmark integrates the TCGA platform, making data resources accessible and usable for machine learning researchers without significant preparation and expertise.To date, CMOB includes a collection of 20 cancer multi-omics datasets covering 32 cancers, accompanied by a systematic data processing pipeline. CMOB provides well-processed dataset versions to support 20 meaningful tasks in four studies, with a collection of benchmarks. We also integrate CMOB with two complementary resources and various biological tools to explore broader research avenues.All resources are open-accessible with user-friendly and compatible integration scripts that enable non-experts to easily incorporate this complementary information for various tasks. We conduct extensive experiments on selected datasets to offer recommendations on suitable machine learning baselines for specific applications. Through CMOB, we aim to facilitate algorithmic advances and hasten the development, validation, and clinical translation of machine-learning models for personalized cancer treatments. CMOB is available on GitHub (\url{https://github.com/chenzRG/Cancer-Multi-Omics-Benchmark}).<|reference_end|>
@article{yang2024cmob:,
    title={CMOB: Large-Scale Cancer Multi-Omics Benchmark with Open Datasets,
  Tasks, and Baselines},
    author={Ziwei Yang, Rikuto Kotoge, Zheng Chen, Xihao Piao, Yasuko Matsubara,
  and Yasushi Sakurai},
    journal={arXiv preprint arXiv:2409.02143},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.02143},
    primaryClass={q-bio.GN cs.LG}
}