Abstract
Recently, individual heterogeneity is becoming a hot topic with the development of precision medicine. It is still a challenge to characterize the intrinsic regulatory convergence along with temporal gene expression change corresponding to different individuals. Considering the similar functions will be more suitable than the same genes to find consistent function rather than chaotic genes, we propose a computational framework (ABP: Attractor analysis of Boolean network of Pathway) to recognize the key pathways associated with phenotype change, which uses the network attractor to represent the steady pathway states corresponding to the final biological sate of individuals. By analyzing temporal gene expressions, ABP has shown its ability to recognize key pathways and infer the potential consensus functional cascade among pathways, and especially group individuals corresponding to disease state well.
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Sun, S., Yu, X., Sun, F., Tang, Y., Zhao, J., Zeng, T. (2018). Characterizing and Discriminating Individual Steady State of Disease-Associated Pathway. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_50
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DOI: https://doi.org/10.1007/978-3-319-95930-6_50
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