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Oznake: computer science;machine learning;bayesian learning
Clinical proteomics is suffering from high hopes generated by reports on apparent biomarkers, most of which could not be later substantiated via validation. This has brought into focus the need for improved methods of finding a panel of clearly defined biomarkers. To examine this problem, urinary pr ...
Leto: 2009 Vir: videolectures.net
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Oznake: computer science;machine learning;bayesian learning;bioinformatics;computational systems biology
much smaller number of samples. Analysing such data is statistically challenging, as the covariates are highly correlated, which results in unstable parameter estimates and inaccurate prediction. To alleviate this problem, we have developed a statistical model which uses a small number of meta-covar ...
Leto: 2010 Vir: videolectures.net
Št. zadetkov: 2
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