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• W2898058461 abstract "In the life science, data show an exponential increase trend with the development of biotechnology. Statistical analysis is a powerful tool to find useful information in tremendous amount of data. However, biological systems always contain serious errors where data-driven biostatistics may meet barriers. Different and complementary approaches are needed. Endogenous network theory is a mechanism-driven method which can quantitatively analyze regulation networks. Steady states expression profiles and potential landscape of a dynamic network could be found by this theory. Simulations of stochastic effects indicate that in systems with large noise, data-driven biostatistics can fail to reveal the true biological mechanism." @default.
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• W2898058461 date "2018-07-01" @default.
• W2898058461 modified "2023-10-16" @default.
• W2898058461 title "Statistical Analysis Can Fail to Reveal Underlying True Biological Mechanism: A Demonstration of Expression Profile Generation" @default.
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• W2898058461 doi "https://doi.org/10.23919/chicc.2018.8484056" @default.
• W2898058461 hasPublicationYear "2018" @default.
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