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An Algorithm to Learn Causal Relations Between Genes from Steady State Data: Simulation and Its Application to Melanoma Dataset

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Artificial Intelligence in Medicine (AIME 2005)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3581))

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Abstract

In recent years, a few researchers have challenged past dogma and suggested methods (such as the IC algorithm) for inferring causal relationship among variables using steady state observations. In this paper, we present a modified IC (mIC) algorithm that uses entropy to test conditional independence and combines the steady state data with partial prior knowledge of topological ordering in gene regulatory network, for jointly learning the causal relationship among genes. We evaluate our mIC algorithm using the simulated data. The results show that the precision and recall rates are significantly improved compared with using IC algorithm. Finally, we apply the mIC algorithm to microarray data for melanoma. The algorithm identified the important causal relations associated with WNT5A, a gene playing an important role in melanoma, verified by the literatures.

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Zhang, X., Baral, C., Kim, S. (2005). An Algorithm to Learn Causal Relations Between Genes from Steady State Data: Simulation and Its Application to Melanoma Dataset. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds) Artificial Intelligence in Medicine. AIME 2005. Lecture Notes in Computer Science(), vol 3581. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11527770_69

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  • DOI: https://doi.org/10.1007/11527770_69

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-27831-3

  • Online ISBN: 978-3-540-31884-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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