Synthetic Time Series Resembling Human (HeLa) Cell-Cycle Gene Expression Data and Application to Gene Regulatory Network Discovery

Tam, Gary Hak Fui; Hung, Y. S.; Chang, Chunqi

doi:10.1109/ihmsc.2013.276

Cited by 2 publications

(3 citation statements)

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“…Thus, distinguishing between direct or indirect influences is also an advantage of GC over other GRN discovery methods. A simple comparison between PCC and GC is given in [34]. Besides, the present VAR modelling [see (1) and (2)] assumes that genes interact with each other linearly.…”

Section: Discussionmentioning

confidence: 99%

Gene regulatory network discovery using pairwise Granger causality

Tam

Chang

Hung

2013

IET Systems Biology

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Discovery of gene regulatory network from gene expression data can yield a useful insight to drug development. Among the methods applied to time-series data, Granger causality (GC) has emerged as a powerful tool with several merits. Since gene expression data usually have a much larger number of genes than time points therefore a full model cannot be applied in a straightforward manner, GC is often applied to genes pair wisely. In this study, the authors first investigate with synthetic data how spurious causalities (false discoveries) may arise because of the use of pairwise rather than full-model GC detection. Furthermore, spurious causalities may also arise if the order of the vector autoregressive model is not high enough. As a remedy, the authors demonstrate that model validation techniques can effectively reduce the number of false discoveries. Then, they apply pairwise GC with model validation to the real human HeLa cell-cycle dataset. They find that Akaike information criterion is generally most suitable for determining model order, but precaution should be taken for extremely short time series. With the authors proposed implementation, degree distributions and network hubs are obtained and compared with existing results, giving a new observation that the hubs tend to act as sources rather than receivers of interactions.

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Section: Discussionmentioning

confidence: 99%

Gene regulatory network discovery using pairwise Granger causality

Tam

Chang

Hung

2013

IET Systems Biology

Self Cite

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“…The gene expression data can be modeled by a vector autoregressive (VAR) model [16,37]. We can directly employ the SMC-SISR estimator instead of first estimating the parameters of this model, In particular, the sequence of the gene expression time series can be modeled recursively by the -order VAR model, i.e., let,…”

Section: Gene Expression Time Series Examplementioning

confidence: 99%

“…The estimator accuracy is evaluated by computer simulations. In addition, the developed estimator is used to infer the missing values in the gene expression time series data modeled as a vector autoregressive (VAR) process [36,37].…”

Section: Introductionmentioning

confidence: 99%

Variational Bayesian inference of hidden stochastic processes with unknown parameters

Atitey,

Loskot,

Mihaylova

2019

Preprint

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Estimating hidden processes from non-linear noisy observations is particularly difficult when the parameters of these processes are not known. This paper adopts a machine learning approach to devise variational Bayesian inference for such scenarios. In particular, a random process generated by the autoregressive moving average (ARMA) linear model is inferred from non-linearity noisy observations. The posterior distributions of hidden states are approximated by a set of weighted particles generated by the sequential Monte Carlo (SMC) algorithm involving sampling with importance samplingresampling (SISR). Numerical efficiency and estimation accuracy of the proposed inference method are evaluated by computer simulations. Furthermore, the proposed inference method is demonstrated on a practical problem of estimating the missing values in the gene expression time series assuming vector autoregressive (VAR) data model.

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Synthetic Time Series Resembling Human (HeLa) Cell-Cycle Gene Expression Data and Application to Gene Regulatory Network Discovery

Cited by 2 publications

References 10 publications

Gene regulatory network discovery using pairwise Granger causality

Gene regulatory network discovery using pairwise Granger causality

Variational Bayesian inference of hidden stochastic processes with unknown parameters

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