Abstract
Detecting the interaction among gene mutations is still an open problem on genetic research. Among various types of interaction, the causality among the gene mutations provides deep insight of the gene mutation and evolution, is the focus of the current research. Different from the global causal network reconstruction method, we propose a local causal discovery method by exploring the causal concept under the association rule discovery framework. Firstly we propose a V-Structure Measure (VSM) to evaluate the causal significance of the local SNPs structures. Secondly, we develop a method called ASymmetric Causal Association Rule Discovery (ASCARD) to mine the reliable causal association rules considering the conflicts among the candidate structures. Finally, the experiments on the synthetic data and WTCCC (Wellcome Trust Case Control Consortium) SNPs dataset shows the effectiveness of the proposed method. Some interesting biological discoveries also show the potential of the real world applications.
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Acknowledgments
This study makes use of data generated by the Wellcome Trust Case-Control Consortium. A full list of the investigators who contributed to the generation of the data is available from www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113, 085475 and 090355. This research was also supported by NSFC-Guangdong Joint Found (U1501254), Natural Science Foundation of China (61876043, 61472089), Natural Science Foundation of Guangdong (2014A030306004, 2014A030308008), Science and Technology Planning Project of Guangdong (2015B010108006, 2015B010131015), Guangdong High-level Personnel of Special Support Program (2015TQ01X140), Pearl River S&T Nova Program of Guangzhou (201610010101), and Science and Technology Planning Project of Guangzhou (201604016075).
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Cai, R., Zhen, Q., Hao, Z. (2018). Identification of Causality Among Gene Mutations Through Local Causal Association Rule Discovery. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11307. Springer, Cham. https://doi.org/10.1007/978-3-030-04239-4_42
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