Abstract
Understanding the complexity of the cellular machinery represents a grand challenge in molecular biology. To contribute to the deconvolution of this complexity, a novel inference algorithm based on linear ordinary differential equations is proposed, based on high-throughput gene expression data. The algorithm can infer (i) gene-gene interactions from steady state expression profiles AND (ii) mode-of-action of the components that can trigger changes in the system. Results demonstrate that the proposed algorithm can identify both information with high performances, thus overcoming the limitation of current algorithms that can infer reliably only one.
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© 2009 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Lai, D., Lu, H., Lauria, M., di Bernardo, D., Nardini, C. (2009). MANIA: A Gene Network Reverse Algorithm for Compounds Mode-of-Action and Genes Interactions Inference. In: Zhou, J. (eds) Complex Sciences. Complex 2009. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02466-5_37
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DOI: https://doi.org/10.1007/978-3-642-02466-5_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02465-8
Online ISBN: 978-3-642-02466-5
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