Abstract
A pattern recognition approach, based on shape feature extraction, is proposed to infer genetic networks from time course microarray data. The proposed algorithm learns patterns from known genetic interactions, such as RT-PCR confirmed gene pairs, and tunes the parameters using particle swarm optimization algorithm. This work also incorporates a score function to separate significant predictions from non-significant ones. The prediction accuracy of the proposed method applied to data sets in Spellman et al. (1998) is as high as 91%, and true-positive rate and false-negative rate are about 61% and 1%, respectively. Therefore, the proposed algorithm may be useful for inferring genetic interactions.
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References
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Chuang, CL., Chen, CM., Shieh, G.S. (2006). A Hybrid Algorithm to Infer Genetic Networks. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_118
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DOI: https://doi.org/10.1007/11893257_118
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
Online ISBN: 978-3-540-46482-2
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