Feature selection for high-dimensional temporal data

Feature selection for high-dimensional temporal data

Abstract Background Feature selection is commonly employed for identifying collectively-predictive biomarkers and biosignatures; it facilitates the construction of small statistical models that are easier to verify, visualize, and comprehend while providing insight to the human expert. In this work...

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Bibliographic Details
Main Authors: Michail Tsagris, Vincenzo Lagani, Ioannis Tsamardinos
Format: Article
Language:English
Published: BMC 2018-01-01
Series:BMC Bioinformatics
Subjects:
Time course data
Longitudinal data
Regression
Variable selection
Multiple solutions
Online Access:http://link.springer.com/article/10.1186/s12859-018-2023-7

Internet

http://link.springer.com/article/10.1186/s12859-018-2023-7

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