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- W4318472321 abstract "Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality and morbidity worldwide. Machine learning (ML) tools have been developed in recent years to generate diagnostic and prognostic molecular biomarkers for this high-fatality cancer. In total, 75 studies met our inclusion criteria, 53 of which were pertinent to diagnosis of HCC and 22 of which were pertinent to prognostication of HCC. The ML algorithms achieved a sensitivity of up to 95% for the diagnosis of HCC. Through pathway analysis of the signatures derived by ML tools, we identified regulators of epithelial-mesenchymal transition and the cancer pathway Ras/Raf/MAPK as being particularly prognostic of HCC outcome. The application of ML to molecular data in HCC has thus far resulted in the generation of highly sensitive diagnostic and prognostic signatures." @default.
- W4318472321 created "2023-01-30" @default.
- W4318472321 date "2022-12-01" @default.
- W4318472321 modified "2023-10-17" @default.
- W4318472321 doi "https://doi.org/10.1111/lci2.v3.4" @default.
- W4318472321 hasPublicationYear "2022" @default.
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