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- W2947771303 abstract "Omics technologies promised improved biomarker discovery for precision medicine. The foremost problem of discovered biomarkers is irreproducibility between patient cohorts. From a data analytics perspective, the main reason for these failures is bias in statistical approaches and overfitting resulting from batch effects and confounding factors. The keys to reproducible biomarker discovery are: proper study design, unbiased data preprocessing and quality control analyses, and a knowledgeable application of statistics and machine learning algorithms. In this review, we discuss study design and analysis considerations and suggest standards from an expert point-of-view to promote unbiased decision-making in biomarker discovery in precision medicine." @default.
- W2947771303 created "2019-06-07" @default.
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- W2947771303 date "2019-09-01" @default.
- W2947771303 modified "2023-10-16" @default.
- W2947771303 title "Unbiased data analytic strategies to improve biomarker discovery in precision medicine" @default.
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- W2947771303 doi "https://doi.org/10.1016/j.drudis.2019.05.018" @default.
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