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...
Paper Details
Title
A closer look at cross-validation for assessing the accuracy of gene regulatory networks and models
Published Date
Apr 26, 2018
Journal
Volume
8
Issue
1
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