PT - JOURNAL ARTICLE AU - Yasset Perez-Riverol AU - Max Kun AU - Juan Antonio Vizcaíno AU - Marc-Phillip Hitz AU - Enrique Audain TI - Accurate and Fast feature selection workflow for high-dimensional omics data AID - 10.1101/144162 DP - 2017 Jan 01 TA - bioRxiv PG - 144162 4099 - http://biorxiv.org/content/early/2017/06/02/144162.short 4100 - http://biorxiv.org/content/early/2017/06/02/144162.full AB - We are moving into the age of ‘Big Data’ in biomedical research and bioinformatics. This trend could be encapsulated in this simple formula: D = S × F, where the volume of data generated (D) increases in both dimensions: the number of samples (S) and the number of sample features (F). Frequently, a typical bioinformatics problem (e.g. classification) includes redundant and irrelevant features that can result, in the worst-case scenario, in false positive results. Then, Feature Selection (FS) constitutes an enormous challenge. Despite the number and diversity of algorithms available, the proper choice of an approach for facing a specific problem often falls in a ‘grey zone’. In this study, we select a subset of FS methods to develop an efficient workflow and an R package for bioinformatics machine learning problems. We cover relevant issues concerning FS, ranging from domain’s problems to algorithm solutions and computational tools. Finally, we use seven different proteomics and gene expression datasets to evaluate the workflow and guide the FS process.CMCorrelation MatrixFSFeature SelectionMLMachine LearningPCAPrincipal Component AnalysisRFERecursive Feature EliminationRMSERoot Mean Square ErrorRFRandom ForestSVMSupport Vector MachineTNBCTriple-Negative Breast CancerX2Univariate Correlation