Abstract
Due to the large number of uncertain factors in hybridization, image capture and processing of the microarray, multiple probes were generally arranged to improve the reliability of the measurement. However, the small area limited the number of probes that were allowed to be added on, so a composite probe would be the better choice. A composite probe contained the linear combination of a variety of gene fragments. It was used so that the microarray could easily realize the repeated gene fragments within a limited region. The number of composite probes would rapidly dwindle when it compared to a traditional microarray. At the same time, since the sparse characteristics of biological gene mutation, the compressed sensing idea is adopted to recovery the gene variation in the composite probes. The 96 fragments can be used with the 48 × 96 sparse random matrix to construct the 48 composite probes when the sparsest level K is no more than 12. Simulation results show that compressed sensing can accurately recover the gene mutation by using the Orthogonal Matching Pursuit (OMP) algorithm.
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Acknowledgments
This work is partially supported by the National Natural Foundation Project (61304199), the Ministry of Science and Technology projects for TaiWan, HongKong and Maco (2012DFM30040), the Major projects in Fujian Province (2013HZ0002-1,2013YZ0002,2014YZ0001), the Science and Technology project in Fujian Province Education Department (JB13140/GY-Z13088), and the Scientific Fund project in Fujian University of Technology (GY-Z13005,GY-Z13125).
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Gan, Z., Xiong, B., Zou, F., Gao, Y., Du, M. (2017). Composite Probe and Signal Recovery of Compressed Sensing Microarray. In: Pan, JS., Lin, JW., Wang, CH., Jiang, X. (eds) Genetic and Evolutionary Computing. ICGEC 2016. Advances in Intelligent Systems and Computing, vol 536. Springer, Cham. https://doi.org/10.1007/978-3-319-48490-7_2
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DOI: https://doi.org/10.1007/978-3-319-48490-7_2
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