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 even multiple biological conditions. For the latter, in a simple case-control study features or genes are selected under the assumption of no change over time for the control samples, while the case group must have at least one change. The proposed model reduces the feature space according to a two-state hidden Markov model. The two states define chan

SUBMITTER: Cardenas-Ovando RA 

PROVIDER: S-EPMC6786538 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature