How (Not) to Generate a Highly Predictive Biomarker Panel Using Machine Learning.

ABSTRACT: This review "teaches" researchers how to make their lackluster proteomics data look really impressive, by applying an inappropriate but pervasive strategy that selects features in a biased manner. The strategy is demonstrated and used to build a classification model with an accuracy of 92% and AUC of 0.98, while relying completely on random numbers for the data set. This "lesson" in data processing is not to be practiced by anyone; on the contrary, it is meant to be a cautionary tale showing that very unreliable results are obtained when a biomarker panel is generated first, using all the available data, and then tested by cross-validation. Data scientists describe the error committed in this scenario as having test data leak into the feature selection step, and it is currently a common mi

SUBMITTER: Desaire H 

PROVIDER: S-EPMC9680826 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature