A feature selection strategy for gene expression time series experiments with hidden Markov models
Abstract
Studies conducted in time series could be far more informative than those that only capture a specific moment in time. However, when it comes to transcriptomic data, time points are sparse creating the need for a constant search for methods capable of extracting information out of experiments of this kind. We propose a feature selection algorithm embedded in a hidden Markov model applied to gene expression time course data on either single or...
Paper Details
Title
A feature selection strategy for gene expression time series experiments with hidden Markov models
Published Date
Oct 10, 2019
Journal
Volume
14
Issue
10
Pages
e0223183 - e0223183
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