NDLI: Multiple reference networks improve accuracy of signaling network construction

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Multiple reference networks improve accuracy of signaling network construction

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Multiple reference networks improve accuracy of signaling network construction

Content Provider	ACM Digital Library
Author	Kahveci, Tamer Thai, My T. Alim, Abdul Ay, Ahmet Hasan, Md Mahmudul
Abstract	Signaling networks play a key role in almost all cellular functions and cell's response to extracellular stimuli. Constructing the topology of interactions that make up signaling networks is of utmost importance to understand how these networks function. Knockdown assays, such as RNA interference (RNAi) technology, provides us hints about the roles of individual genes involved in these networks. However, determining the correct topology of interactions between these genes remains to be challenging. Existing computational approaches are either not scalable to large networks, or they have low accuracies. In this study, we consider that problem of constructing signaling network topology from single gene knockdown experiments. We formulate this mathematically as a network editing problem and develop an efficient algorithm to solve this problem. The key contribution in this paper is that our formulation can integrate the knowledge available in the form of the topologies of an arbitrary number of reference networks. Our experiments on synthetic, semi-synthetic and real datasets demonstrate that the proposed method greatly outperforms the state of the art methods, which are limited to a single reference network, in terms of accuracy with negligible increase in computational cost. Furthermore, our experiments suggest that the accuracy of our method remains high even when evolutionarily distant reference networks are used. Application of our method to the Apoptosis and Wnt signaling pathways recovers a large number of known protein-protein interactions.
Starting Page	176
Ending Page	185
Page Count	10
File Format	PDF
ISBN	9781450338530
DOI	10.1145/2808719.2808737
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-09-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Systems biology Signaling network Reference network Network reconstruction
Content Type	Text
Resource Type	Article

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