Nearest Template Prediction: A Single-Sample-Based Flexible Class Prediction with Confidence Assessment

By Yujin Hoshida. PLoS ONE, 2010

326 citations recorded by the source corpus.

Gene-expression signature-based disease classification and clinical outcome prediction has not been widely introduced in clinical medicine as initially expected, mainly due to the lack of extensive validation needed for its clinical deployment. Obstacles include variable measurement in microarray assay, inconsistent assay platform, analytical requirement for comparable pair of training and test datasets, etc. Furthermore, as medical device helping clinical decision making, the prediction needs to be made for each single patient with a measure of its reliability. To address these issues, there is a need for flexible prediction method less sensitive to difference in experimental and analytical conditions, applicable to each single patient, and providing measure of prediction confidence. The nearest template prediction (NTP) method provides a convenient way to make class prediction with assessment of prediction confidence computed in each single patient's gene-expression data using only a list of signature genes and a test dataset. We demonstrate that the method can be flexibly applied to cross-platform, cross-species, and multiclass predictions without any optimization of analysis parameters.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/32b2cea32b584b4c71930b14115c8d37fd931219

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In Molecular Biology

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Citations in the corpus

• CMScaller: an R package for consensus molecular subtyping of colorectal cancer pre-clinical models (2017, methodology): “The intersection of candidate, robust and intrinsic markers were used as template features for nearest template prediction 33 , which is a correlation-based algorithm developed to provide robust class prediction for high-dimensional, noisy gene expression data, and which has been successfully…”