Characterizing Gene Expression Time Series using a Hidden Markov Model

2006 ◽  
pp. 37-50
Author(s):  
Sally McClean ◽  
Bryan Scotney ◽  
Steve Robinson
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Gene Expression Time Series ◽  
Expression Time

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

2018 ◽  
Author(s):  
Roberto A. Cárdenas-Ovando ◽  
Edith A. Fernández-Figueroa ◽  
Héctor A. Rueda-Zárate ◽  
Julieta Noguez ◽  
Claudia Rangel-Escarenõ
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Feature Selection ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Selection Strategy ◽  
Case Group ◽  
Time Points ◽  
Expression Time

AbstractStudies conducted in time series could be far more informative than those questioning at a specific moment in time. However, when it comes to genomic 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 change/no-change in gene expression. Features are ranked in consonance with three scores: number of changes across time, magnitude of such changes and quality of replicates as a measure of how much they deviate from the mean. An important highlight is that this strategy overcomes the few samples limitation, common in genomic experiments through a process of data transformation and rearrangement. To prove this method, our strategy was applied to three publicly available data sets. Results show that feature domain is reduced to up to 90% leaving only few but relevant features yet with findings consistent to those previously reported. Moreover, our strategy proved to be robust, stable and working on studies where sample size is an issue otherwise. Hence, even with two biological replicates and/or three time points our method proves to work well.

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

PLoS ONE ◽  
2019 ◽  
Vol 14 (10) ◽  
pp. e0223183 ◽  
Author(s):  
Roberto A. Cárdenas-Ovando ◽  
Edith A. Fernández-Figueroa ◽  
Héctor A. Rueda-Zárate ◽  
Julieta Noguez ◽  
Claudia Rangel-Escareño
Keyword(s):  
Gene Expression ◽  
Time Series ◽  
Feature Selection ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Selection Strategy ◽  
Gene Expression Time Series ◽  
Expression Time

scholarly journals Shift Detection in Hydrological Regimes and Pluriannual Low-Frequency Streamflow Forecasting Using the Hidden Markov Model

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2058 ◽  
Author(s):  
Larissa Rolim ◽  
Francisco de Souza Filho
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Extreme Events ◽  
Hidden Markov ◽  
Low Frequency ◽  
Streamflow Forecasting ◽  
Hydrologic Time Series ◽  
Low Frequency Variability ◽  
The Impact

Improved water resource management relies on accurate analyses of the past dynamics of hydrological variables. The presence of low-frequency structures in hydrologic time series is an important feature. It can modify the probability of extreme events occurring in different time scales, which makes the risk associated with extreme events dynamic, changing from one decade to another. This article proposes a methodology capable of dynamically detecting and predicting low-frequency streamflow (16–32 years), which presented significance in the wavelet power spectrum. The Standardized Runoff Index (SRI), the Pruned Exact Linear Time (PELT) algorithm, the breaks for additive seasonal and trend (BFAST) method, and the hidden Markov model (HMM) were used to identify the shifts in low frequency. The HMM was also used to forecast the low frequency. As part of the results, the regime shifts detected by the BFAST approach are not entirely consistent with results from the other methods. A common shift occurs in the mid-1980s and can be attributed to the construction of the reservoir. Climate variability modulates the streamflow low-frequency variability, and anthropogenic activities and climate change can modify this modulation. The identification of shifts reveals the impact of low frequency in the streamflow time series, showing that the low-frequency variability conditions the flows of a given year.

Heuristic hidden Markov model for fuzzy time series forecasting

2021 ◽  
Vol 20 (2) ◽  
pp. 146
Author(s):  
Ahmed T. Salawudeen ◽  
Patrick J. Nyabvo ◽  
Hussein U. Suleiman ◽  
Izuagbe S. Momoh ◽  
Emmanuel K. Akut
Keyword(s):  
Time Series ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Time Series Forecasting ◽  
Fuzzy Time Series
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