@inproceedings{5e3639da2b274780aa95f2ff464f7b18,
title = "An algorithm to learn causal relations between genes from steady state data: Simulation and its application to melanoma dataset",
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.",
author = "Xin Zhang and Chitta Baral and Seungchan Kim",
year = "2005",
doi = "10.1007/11527770_69",
language = "English (US)",
isbn = "3540278311",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "524--534",
booktitle = "Artificial Intelligence in Medicine - 10th Conference on Artificial Intelligence in Medicine, AIME 2005, Proceedings",
note = "10th Conference on Artificial Intelligence in Medicine, AIME 2005 ; Conference date: 23-07-2005 Through 27-07-2005",
}