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| Name | rankDetect |
|---|---|
| Type | Health and Disease Detection |
| Version | v1.0 |
| Developers | Yadong Yang, Tao Zhang, Rudan Xiao, Xiangdong Fang |
| Description | Peripheral blood gene expression intensity-based methods for distinguishing healthy individuals from cancer patients are limited by sensitivity to batch effects and data normalization and variability between expression profiling assays. To improve the robustness and precision of blood gene expression-based tumour detection, it is necessary to perform molecular diagnostic tests using a more stable approach. Taking breast cancer as an example, we propose a machine learning–based framework that distinguishes breast cancer patients from healthy subjects by pairwise rank transformation of gene expression intensity in each sample. The rank-based self-learning model can offer valuable information for breast cancer diagnosis and is insensitive to batch effects and data normalization. |
| Downlaod | https://ngdc.cncb.ac.cn/biocode/tools/BT007224 |
| Article | https://doi.org/10.1093/bib/bbz027 |
| Cite Count | 8 |
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