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Mathematical Biosciences and Engineering

2016, Volume 13, Issue 6: 1241-1251. doi: 10.3934/mbe.2016041
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A posterior probability approach for gene regulatory network inference in genetic perturbation data

  • 1.
    University of Washington, Department of Statistics, Box 354322, Seattle, WA 98195-4322
  • 2.
    University of Washington, Institute of Technology, Box 358426, 1900 Commerce Street, Tacoma, WA 98402-3100
  • Received: 01 September 2015 Accepted: 29 June 2018 Published: 01 August 2016

  • MSC : Primary: 62P10, 92D10; Secondary: 92C42.

  • Inferring gene regulatory networks is an important problem in systems biology. However, these networks can be hard to infer from experimental data because of the inherent variability in biological data as well as the large number of genes involved. We propose a fast, simple method for inferring regulatory relationships between genes from knockdown experiments in the NIH LINCS dataset by calculating posterior probabilities, incorporating prior information. We show that the method is able to find previously identified edges from TRANSFAC and JASPAR and discuss the merits and limitations of this approach.

    Citation: William Chad Young, Adrian E. Raftery, Ka Yee Yeung. A posterior probability approach for gene regulatory network inference in genetic perturbation data[J]. Mathematical Biosciences and Engineering, 2016, 13(6): 1241-1251. doi: 10.3934/mbe.2016041

    Related Papers:

  • Inferring gene regulatory networks is an important problem in systems biology. However, these networks can be hard to infer from experimental data because of the inherent variability in biological data as well as the large number of genes involved. We propose a fast, simple method for inferring regulatory relationships between genes from knockdown experiments in the NIH LINCS dataset by calculating posterior probabilities, incorporating prior information. We show that the method is able to find previously identified edges from TRANSFAC and JASPAR and discuss the merits and limitations of this approach.


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    [1] Bioinformatics, 22 (2006), 815-822.
    [2] Nature Genetics, 37 (2005), 382-390.
    [3] Annual Review of Statistics and Its Application, 1 (2014), 255-278.
    [4] BMC Bioinformatics, 14 (2013), 128-141.
    [5] PLoS One, 4 (2009), e6799.
    [6] Statistical Science, 19 (2004), 81-94.
    [7] Journal of the Royal Statistical Society. Series B (Methodological), 39 (1977), 1-38.
    [8] Pacific Symposium on Biocomputing, 4 (1999), 41-52.
    [9] Bioinformatics Conference, 2003. CSB 2003. Proceedings of the 2003 IEEE , (2003), 523-528.
    [10] http://www.synapse.org/#!Synapse:syn3049712/wiki/74628
    [11] Nucleic Acids Research, 42 (2014), w449-w460.
    [12] Clinica Chimica Acta, 363 (2006), 71-82.
    [13] PLoS Biol, 5 (2007), e8.
    [14] RECOMB '00 Proceedings of the fourth annual international conference on Computational molecular biology, (2000), 127-135.
    [15] BMC Bioinformatics, 8 (2007), 386.
    [16] Nature Genetics, 31 (2002), 60-63.
    [17] Annals of the New York Academy of Sciences, 1158 (2009), 265-275.
    [18] Biosystems, 96 (2009), 86-103.
    [19] Statistical Science, 14 (1999), 382-417.
    [20] Journal of the American Statistical Association, 90 (1995), 773-795.
    [21] Briefings in Bioinformatics, 4 (2003), 228-235.
    [22] Computational Methods in Systems Biology, 2602 (2003), 104-113.
    [23] Bioinformatics, 26 (2010), 2160-2168.
    [24] BMC Systems Biology, 4 (2010), article 130.
    [25] Briefings in Bioinformatics, 10 (2009), 408-423.
    [26] Statistical Methods in Medical Research, 22 (2013), 519-536.
    [27] Bioinformatics, 27 (2011), 2686-2691.
    [28] http://lincsproject.org/
    [29] BMC Systems Biology, 6 (2012), p101.
    [30] BMC Systems Biology, 5 (2011), p61.
    [31] PLoS Computational Biology, 10 (2014), e1003676.
    [32] Journal of Computational Biology, 16 (2009), 229-239.
    [33] Proceedings of the National Academy of Sciences, 107 (2010), 6286-6291.
    [34] BMC Bioinformatics, 7 (2006), S7.
    [35] BMC Bioinformatics, 8 (2007), S5.
    [36] PLoS One, 5 (2010), e14147.
    [37] EURASIP Journal on Bioinformatics and Systems Biology, 2007 (2007), 79879.
    [38] BMC Bioinformatics, 9 (2008), article 461.
    [39] Mathematical Biosciences, 246 (2013), 326-334.
    [40] Vol. 104. Technical report, Computer Science Division, University of California, Berkeley, CA, 1999.
    [41] PLoS One, 5 (2010), e12912.
    [42] Journal of the American Statistical Association, 92 (1997), 179-191.
    [43] Sociological Methods & Research, 27 (1999), 411-417.
    [44] Bioinformatics, 21 (2005), 3131-3137.
    [45] Computational Biology and Chemistry, 59 (2015), 3-14.
    [46] Signal Processing Conference (EUSIPCO), 2013 Proceedings of the 21st European, (2013), 1-4.
    [47] Nucleic Acids Research, 32 (2004), D91-D94.
    [48] Journal of Statistical Software, 35 (2010), 1-22.
    [49] Journal of Computational Biology, 16 (2009), 407-426.
    [50] Bioinformatics, 26 (2010), i517-i523.
    [51] PLoS One, 9 (2014), e82393.
    [52] Journal of the Royal Statistical Society. Series B (Methodological), 58 (1996), 267-288.
    [53] Proceedings of the National Academy of Sciences, 98 (2001), 5116-5121.
    [54] Electronic Journal of Statistics, 6 (2012), 38-90.
    [55] IEEE Transactions on Information Theory, 55 (2009), 2183-2202.
    [56] Nucleic Acids Research, 28 (2000), 316-319.
    [57] Proceedings of the National Academy of Sciences, 108 (2011), 19436-19441.
    [58] Pacific Symposium on Biocomputing, 7 (2002), 498-509.
    [59] BMC Systems Biology, 8 (2014), 47-57.
    [60] Bayesian Inference and Decision Techniques: Essays in Honor of Bruno De Finetti, 6 (1986), 233-243.
    [61] BMC Bioinformatics, 11 (2010), p154.
    [62] Bioinformatics, 21 (2005), 71-79.
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  • © 2016 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
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