Inferring signaling pathways using interventional data

Article type: Research Article

Authors: Mazloomian, Alborz | Beigy, Hamid^{; *}

Affiliations: Department of Computer Engineering, Sharif University of Technology, Azadi Ave, Tehran, Iran

Correspondence: [*] Corresponding author: Hamid Beigy, Department of Computer Engineering, Sharif University of Technology, Azadi Ave, Tehran, Iran. Tel.: +98 21 6616 6602; E-mail: beigy@sharif.ir.

Abstract: Studying biological networks helps to gain a better understanding of cellular behaviors. One of the prominent models to study complex interactions in biological networks is the Nested Effects Model (NEM). Based on the Nested Effects Model, we propose two methods for inferring signaling pathways from interventional data. In the first method, we search the space of all feasible solutions with an evolutionary approach to maximize a standard Bayesian score. In the second method, sub-models are constructed with informative features and then combined using an averaging method to make the analysis of larger networks computationally possible. We tested our proposed methods in various noise levels on real and artificial networks with different sizes. The networks constructed by our method have a higher level of accuracy compared to the networks inferred by the triplets method introduced by Markowetz. Moreover, our results show a high level of robustness.

Keywords: Biological networks, Nested Effects Model, signaling pathways, evolutionary approach, averaging method

DOI: 10.3233/IDA-130579

Journal: Intelligent Data Analysis, vol. 17, no. 2, pp. 295-308, 2013