MLOmics: Cancer Multi-Omics Database for Machine Learning
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arXiv
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| Hauptverfasser: | , , , , , , , , |
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| Format: | Preprint |
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2024
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| author | Yang, Ziwei Kotoge, Rikuto Piao, Xihao Chen, Zheng Zhu, Lingwei Gao, Peng Matsubara, Yasuko Sakurai, Yasushi Sun, Jimeng |
| author_facet | Yang, Ziwei Kotoge, Rikuto Piao, Xihao Chen, Zheng Zhu, Lingwei Gao, Peng Matsubara, Yasuko Sakurai, Yasushi Sun, Jimeng |
| contents | Framing the investigation of diverse cancers as a machine learning problem has recently shown significant potential in multi-omics analysis and cancer research. Empowering these successful machine learning models are the high-quality training datasets with sufficient data volume and adequate preprocessing. However, while there exist several public data portals, including The Cancer Genome Atlas (TCGA) multi-omics initiative or open-bases such as the LinkedOmics, these databases are not off-the-shelf for existing machine learning models. In this paper, we introduce MLOmics, an open cancer multi-omics database aiming at serving better the development and evaluation of bioinformatics and machine learning models. MLOmics contains 8,314 patient samples covering all 32 cancer types with four omics types, stratified features, and extensive baselines. Complementary support for downstream analysis and bio-knowledge linking are also included to support interdisciplinary analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_02143 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MLOmics: Cancer Multi-Omics Database for Machine Learning Yang, Ziwei Kotoge, Rikuto Piao, Xihao Chen, Zheng Zhu, Lingwei Gao, Peng Matsubara, Yasuko Sakurai, Yasushi Sun, Jimeng Genomics Machine Learning Framing the investigation of diverse cancers as a machine learning problem has recently shown significant potential in multi-omics analysis and cancer research. Empowering these successful machine learning models are the high-quality training datasets with sufficient data volume and adequate preprocessing. However, while there exist several public data portals, including The Cancer Genome Atlas (TCGA) multi-omics initiative or open-bases such as the LinkedOmics, these databases are not off-the-shelf for existing machine learning models. In this paper, we introduce MLOmics, an open cancer multi-omics database aiming at serving better the development and evaluation of bioinformatics and machine learning models. MLOmics contains 8,314 patient samples covering all 32 cancer types with four omics types, stratified features, and extensive baselines. Complementary support for downstream analysis and bio-knowledge linking are also included to support interdisciplinary analysis. |
| title | MLOmics: Cancer Multi-Omics Database for Machine Learning |
| topic | Genomics Machine Learning |
| url | https://arxiv.org/abs/2409.02143 |