Abstract
Methods called pathway analysis have emerged whose purpose is to identify significantly impacted signaling pathways in a given condition. Most of these methods employ graphs to model the interactions between genes. Graphs have some limitations in accurately modeling various aspects of the interactions in the signaling pathways. As a result, formal methods as practiced in computer science is suggested for modeling signaling pathways. Using formal methods, various types of interactions among biological components are modeled, which can reduce the false-positive rates compared to other methods. Formal methods can also model the concurrent and stochastic behavior of signaling pathways.
In this article, we illustrate how to employ a formal method for pathway analysis and then to evaluate its performance compared to other methods. Results show that the false-positive rate of a formal method approach is lower than other well-known methods. It is also shown that a formal method approach can identify impacted pathways in pancreatic cancer effectively. Furthermore, it can successfully recognize expecting pathways differentiated between African-American and European-American patients in prostate cancer.
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Mansoori, F., Rahgozar, M., Kavousi, K. (2020). Identifying Cancer-Related Signaling Pathways Using Formal Methods. In: Bohlouli, M., Sadeghi Bigham, B., Narimani, Z., Vasighi, M., Ansari, E. (eds) Data Science: From Research to Application. CiDaS 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 45. Springer, Cham. https://doi.org/10.1007/978-3-030-37309-2_11
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