A comparison of methods for data-driven cancer outlier discovery, and an application scheme to semisupervised predictive biomarker discovery.
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ABSTRACT:
SUBMITTER: Karrila S
PROVIDER: S-EPMC3091411 | biostudies-literature | 2011 Apr
REPOSITORIES: biostudies-literature
Publications
A comparison of methods for data-driven cancer outlier discovery, and an application scheme to semisupervised predictive biomarker discovery.
Karrila Seppo S Lee Julian Hock Ean JH Tucker-Kellogg Greg G
Cancer informatics 20110418
A core component in translational cancer research is biomarker discovery using gene expression profiling for clinical tumors. This is often based on cell line experiments; one population is sampled for inference in another. We disclose a semisupervised workflow focusing on binary (switch-like, bimodal) informative genes that are likely cancer relevant, to mitigate this non-statistical problem. Outlier detection is a key enabling technology of the workflow, and aids in identifying the focus genes ...[more]
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