Abstract
Observations of systematic gene perturbation experiments have been proven the most informative for the identification of regulatory relations between genes. For this purpose, we present a novel Qualitative Reasoning approach, based on a qualitative abstraction of DNA-microarray data and on a set of IF-THEN inference rules. Our algorithm exhibits an extremely low rate of false positives, competitive with the state-of-the-art, on both noise-free and noisy simulated data. This, together with the polynomial running time, makes our algorithm an useful tool for systematic gene perturbation experiments, able to identify a subset of the oriented regulatory relations with high reliability and to provide valuable insights on the amount of information conveyed by a set of experiments.
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References
Albert, R.: Scale-free networks in cell biology. Journal of Cell Science 118, 4947–4957 (2005)
Di Camillo, B., Toffolo, G., Cobelli, C.: A gene network simulator to assess reverse engineering algorithms. Annals of the New York Academy of Sciences 1158(1), 125–142 (2009)
Di Camillo, B., Sanchez-Cabo, F., Toffolo, G., Nair, S.K., Trajanoski, Z., Cobelli, C.: A quantization method based on threshold optimization for microarray short time series. BMC Bioinformatics 6, S11 (2005)
Hunter, L.: Life and its molecules: A brief introduction. AI Magazine - Special issue on AI and Bioinformatics 25(1), 9–22 (2004)
Marbach, D., Schaffter, T., Mattiussi, C., Floreano, D.: Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods. Journal of Computational Biology 16(2), 229–239 (2009)
Molla, M., Waddell, M., Page, D., Shavlik, J.: Using machine learning to design and interpret gene-expression microarrays. AI Magazine - Special issue on AI and Bioinformatics 25(1), 23–44 (2004)
Schäfer, J., Strimmer, K.: An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21(6), 754–764 (2005)
Soranzo, N., Bianconi, G., Altafini, C.: Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data. Bioinformatics 23(13), 1640–1647 (2007)
Stolovitzky, G., Monroe, D.O.N., Califano, A.: Dialogue on reverse-engineering assessment and methods: The DREAM of high-throughput pathway inference. Annals of the New York Academy of Sciences 1115(1), 1–22 (2007)
Stolovitzky, G., Prill, R.J., Califano, A.: Lessons from the DREAM2 challenges: A community effort to assess biological network inference. Annals of the New York Academy of Sciences 1158(1), 159–195 (2009)
Zupan, B., Bratko, I., Demsar, J., Juvan, P., Curk, T., Borstnik, U., Beck, J.R., Halter, J.A., Kuspa, A., Shaulsky, G.: GenePath: a system for inference of genetic networks and proposal of genetic experiments. Artificial Intelligence in Medicine 29(1-2), 107–130 (2003)
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Sambo, F., Di Camillo, B. (2011). Qualitative Reasoning on Systematic Gene Perturbation Experiments. In: Rizzo, R., Lisboa, P.J.G. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2010. Lecture Notes in Computer Science(), vol 6685. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21946-7_11
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DOI: https://doi.org/10.1007/978-3-642-21946-7_11
Publisher Name: Springer, Berlin, Heidelberg
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