Abstract

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 x 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 domains 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.

Details

Title
Accurate And Fast Feature Selection Workflow For High-Dimensional Omics Data
Author
Perez-Riverol, Yasset; Kun, Max; Vizcaino, Juan Antonio; Marc-Phillip Hitz; Audain, Enrique
University/institution
Cold Spring Harbor Laboratory Press
Section
New Results
Publication year
2017
Publication date
Jun 2, 2017
Publisher
Cold Spring Harbor Laboratory Press
ISSN
2692-8205
Source type
Working Paper
Language of publication
English
ProQuest document ID
2071237311
Copyright
�� 2017. This article is published under http://creativecommons.org/licenses/by/4.0/ (���the License���). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.