Please use this identifier to cite or link to this item:
|Title:||A comparison of methods for data-driven cancer outlier discovery, and an application scheme to semisupervised predictive biomarker discovery|
|Citation:||Karrila, S., Lee, J.H.E., Tucker-Kellogg, G. (2011). A comparison of methods for data-driven cancer outlier discovery, and an application scheme to semisupervised predictive biomarker discovery. Cancer Informatics 10 : 109-120. ScholarBank@NUS Repository. https://doi.org/10.4137/CIN.S6868|
|Abstract:||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. We compare outlier detection techniques MOST, LSOSS, COPA, ORT, OS, and t-test, using a publicly available NSCLC dataset. Removing genes with Gaussian distribution is computationally efficient and matches MOST particularly well, while also COPA and OS pick prognostically relevant genes in their top ranks. Also our stability assessment is in favour of both MOST and COPA; the latter does not pair well with prefiltering for non-Gaussianity, but can handle data sets lacking non-cancer cases. We provide R code for replicating our approach or extending it. © the author(s), publisher and licensee Libertas Academica Ltd.|
|Source Title:||Cancer Informatics|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Aug 17, 2018
WEB OF SCIENCETM
checked on Jul 9, 2018
checked on Jul 27, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.