### Abstract

Motivation: Interaction among time series can be explored in many ways. All the approach has the usual problem of low power and high dimensional model. Here we attempted to build a causality network among a set of time series. The causality has been established by Granger causality, and then constructing the pathway has been implemented by finding the Minimal Spanning Tree within each connected component of the inferred network. False discovery rate measurement has been used to identify the most significant causalities. Results: Simulation shows good convergence and accuracy of the algorithm. Robustness of the procedure has been demonstrated by applying the algorithm in a non-stationary time series setup. Application of the algorithm in a real dataset identified many causalities, with some overlap with previously known ones. Assembled network of the genes reveals features of the network that are common wisdom about naturally occurring networks.

Original language | English (US) |
---|---|

Pages (from-to) | 442-449 |

Number of pages | 8 |

Journal | Bioinformatics |

Volume | 23 |

Issue number | 4 |

DOIs | |

State | Published - Feb 15 2007 |

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### Cite this

*Bioinformatics*,

*23*(4), 442-449. https://doi.org/10.1093/bioinformatics/btl598

**Causality and pathway search in microarray time series experiment.** / Mukhopadhyay, Nitai D.; Chatterjee, Snigdhansu.

Research output: Contribution to journal › Article

*Bioinformatics*, vol. 23, no. 4, pp. 442-449. https://doi.org/10.1093/bioinformatics/btl598

}

TY - JOUR

T1 - Causality and pathway search in microarray time series experiment

AU - Mukhopadhyay, Nitai D.

AU - Chatterjee, Snigdhansu

PY - 2007/2/15

Y1 - 2007/2/15

N2 - Motivation: Interaction among time series can be explored in many ways. All the approach has the usual problem of low power and high dimensional model. Here we attempted to build a causality network among a set of time series. The causality has been established by Granger causality, and then constructing the pathway has been implemented by finding the Minimal Spanning Tree within each connected component of the inferred network. False discovery rate measurement has been used to identify the most significant causalities. Results: Simulation shows good convergence and accuracy of the algorithm. Robustness of the procedure has been demonstrated by applying the algorithm in a non-stationary time series setup. Application of the algorithm in a real dataset identified many causalities, with some overlap with previously known ones. Assembled network of the genes reveals features of the network that are common wisdom about naturally occurring networks.

AB - Motivation: Interaction among time series can be explored in many ways. All the approach has the usual problem of low power and high dimensional model. Here we attempted to build a causality network among a set of time series. The causality has been established by Granger causality, and then constructing the pathway has been implemented by finding the Minimal Spanning Tree within each connected component of the inferred network. False discovery rate measurement has been used to identify the most significant causalities. Results: Simulation shows good convergence and accuracy of the algorithm. Robustness of the procedure has been demonstrated by applying the algorithm in a non-stationary time series setup. Application of the algorithm in a real dataset identified many causalities, with some overlap with previously known ones. Assembled network of the genes reveals features of the network that are common wisdom about naturally occurring networks.

UR - http://www.scopus.com/inward/record.url?scp=33847348163&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=33847348163&partnerID=8YFLogxK

U2 - 10.1093/bioinformatics/btl598

DO - 10.1093/bioinformatics/btl598

M3 - Article

C2 - 17158516

AN - SCOPUS:33847348163

VL - 23

SP - 442

EP - 449

JO - Bioinformatics

JF - Bioinformatics

SN - 1367-4803

IS - 4

ER -