Abstract
Inferring novel findings from known biological knowledge is one of the ultimate goals in systems biology. However, the observation of system-level responses to a given perturbation has not been thoroughly explored due to the lack of proper large-scale inference models. We developed a novel expert system that can be applied to conventional biological networks based on the production rule system which works by transforming networks into a knowledgebase. Testing on large-scale multi-level biomedical networks confirmed the applicability of our system and revealed that hundreds of molecules are affected by the cascades of given signals, thereby activating or repressing key pathways in a cell.
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Acknowledgement
This work was supported by the Bio-Synergy Research Project (NRF-2014M3A9C4066449) of the Ministry of Science, ICT and Future Planning through the National Research Foundation.
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Kim, S., Nam, H. (2017). In Silico Simulation of Signal Cascades in Biomedical Networks Based on the Production Rule System. In: Cai, Z., Daescu, O., Li, M. (eds) Bioinformatics Research and Applications. ISBRA 2017. Lecture Notes in Computer Science(), vol 10330. Springer, Cham. https://doi.org/10.1007/978-3-319-59575-7_34
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DOI: https://doi.org/10.1007/978-3-319-59575-7_34
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