A feature selection strategy for gene expression time series experiments with hidden Markov models

Abstract

1Studies conducted in time series could be far more informative than those questioning 2 at a specific moment in time. However, when it comes to genomic data, time points are changes and quality of replicates as a measure of how much they deviate from the mean. 13 An important highlight is that this strategy overcomes the few samples limitation, 14 common in genomic experiments through a process of data transformation and 15 rearrangement. To prove this method, our strategy was applied to three publicly 15, 2018; available data sets. Results show that feature domain is reduced to up to 90% leaving 17 only few but relevant features yet with findings consistent to those previously reported. 18 Moreover, our strategy proved to be robust, stable and working on studies where sample 19 size is an issue otherwise. Hence, even with two biological replicates and/or three time 20 points our method proves to work well.is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint . http://dx.doi.org/10.1101/392761 doi: bioRxiv preprint first posted online Aug. 21Author summary

Resting-state EEG dynamics help explain differences in response control in ADHD: Insight into electrophysiological mechanisms and sex differences

“…HMM analyses were conducted using the R package RcppHMM [57]. Observations were z-score normalized and model parameters were randomly initialized using initGHMM.…”