Abstract
Gene association plays important roles in complex genetic pathology of cancer. However, development of methods for finding cancer-related gene associations is still in its infancy. Based on a biological concept of gene association module (GAM) comprising a center gene and its expression-related genes, this paper proposes a gene association detection model called kernel GAM (kGAM). In the model, we assume that the expression of the center gene can be predicted by the expression-related genes. Based on defining a cost function, a kernel ridge regression algorithm is developed to solve the kGAM model. Finally, to identify a compact GAM for a given center gene, a heuristic search procedure is designed. Experimental results on three publicly available gene expression data sets show the effectiveness and efficiency of the proposed kGAM model in identifying cancer-related gene association patterns.
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Wang, HQ., Xie, XP., Li, D. (2012). A New Method for Identifying Cancer-Related Gene Association Patterns. In: Huang, DS., Gan, Y., Premaratne, P., Han, K. (eds) Bio-Inspired Computing and Applications. ICIC 2011. Lecture Notes in Computer Science(), vol 6840. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24553-4_17
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DOI: https://doi.org/10.1007/978-3-642-24553-4_17
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