Abstract
Biomedical research results are being published at a high rate, and with existing search engines, the vast amount of published work is usually easily accessible. However, reproducing published results, either experimental data or observations is often not viable. In this work, we propose a framework to overcome some of the issues of reproducing previous research, and to ensure re-usability of published information. We present here a framework that utilizes the results from state-of-the-art biomedical literature mining, biological system modeling and analysis techniques, and provides means to scientists to assemble and reason about information from voluminous, fragmented and sometimes inconsistent literature. The overall process of automated reading, assembly and reasoning can speed up discoveries from the order of decades to the order of hours or days. Our framework described here allows for rapidly conducting thousands of in silico experiments that are designed as part of this process.
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Acknowledgement
We would like to thank Mihai Surdeanu (REACH team) and Hans Chalupsky (RUBICON team) for providing output of their reading and assembly engines, and Michael Lotze for his guidance in studying cancer microenvironment. This work is supported by DARPA award W911NF-14-1-0422.
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Liang, KW., Wang, Q., Telmer, C., Ravichandran, D., Spirtes, P., Miskov-Zivanov, N. (2017). Methods to Expand Cell Signaling Models Using Automated Reading and Model Checking. In: Feret, J., Koeppl, H. (eds) Computational Methods in Systems Biology. CMSB 2017. Lecture Notes in Computer Science(), vol 10545. Springer, Cham. https://doi.org/10.1007/978-3-319-67471-1_9
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