A feature selection strategy for gene expression time series experiments with hidden Markov models.
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 thi...
Main Authors: | Roberto A Cárdenas-Ovando, Edith A Fernández-Figueroa, Héctor A Rueda-Zárate, Julieta Noguez, Claudia Rangel-Escareño |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2019-01-01
|
Series: | PLoS ONE |
Online Access: | https://doi.org/10.1371/journal.pone.0223183 |
Similar Items
-
A computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database.
by: Héctor A Rueda-Zárate, et al.
Published: (2017-01-01) -
Feature Selection for Hidden Markov Models and Hidden Semi-Markov Models
by: Stephen Adams, et al.
Published: (2016-01-01) -
Hidden Markov Model for Stock Selection
by: Nguyet Nguyen, et al.
Published: (2015-10-01) -
Global Stock Selection with Hidden Markov Model
by: Nguyet Nguyen, et al.
Published: (2020-12-01) -
Hidden Markov and other models for discrete-valued time series /
by: MacDonald, Iain L, et al.
Published: (1997)