A comparison of rule-based and centroid single-sample multiclass predictors for transcriptomic classification.

ABSTRACT: Gene expression-based multiclass prediction, such as tumor subtyping, is a non-trivial bioinformatic problem. Most classifier methods operate by comparing expression levels relative to other samples. Methods that base predictions on the expression pattern within a sample have been proposed as an alternative. As these methods are invariant to the cohort composition and can be applied to a sample in isolation, they can collectively be termed single sample predictors (SSP). Such predictors could potentially be used for preprocessing-free classification of new samples and be built to function across different expression platforms where proper batch and dataset normalization is challenging. Here we evaluate the behavior of several multiclass single sample predictors based on binary gene-pair ru

SUBMITTER: Eriksson P 

PROVIDER: S-EPMC8796360 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature