A closer look at cross-validation for assessing the accuracy of gene regulatory networks and models.

ABSTRACT: Cross-validation (CV) is a technique to assess the generalizability of a model to unseen data. This technique relies on assumptions that may not be satisfied when studying genomics datasets. For example, random CV (RCV) assumes that a randomly selected set of samples, the test set, well represents unseen data. This assumption doesn't hold true where samples are obtained from different experimental conditions, and the goal is to learn regulatory relationships among the genes that generalize beyond the observed conditions. In this study, we investigated how the CV procedure affects the assessment of supervised learning methods used to learn gene regulatory networks (or in other applications). We compared the performance of a regression-based method for gene expression prediction estimated us

SUBMITTER: Tabe-Bordbar S 

PROVIDER: S-EPMC5920056 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature