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Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks

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Gene Regulatory Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1883))

Abstract

Transcriptional regulatory networks specify the regulatory proteins of target genes that control the context-specific expression levels of genes. With our ability to profile the different types of molecular components of cells under different conditions, we are now uniquely positioned to infer regulatory networks in diverse biological contexts such as different cell types, tissues, and time points. In this chapter, we cover two main classes of computational methods to integrate different types of information to infer genome-scale transcriptional regulatory networks. The first class of methods focuses on integrative methods for specifically inferring connections between transcription factors and target genes by combining gene expression data with regulatory edge-specific knowledge. The second class of methods integrates upstream signaling networks with transcriptional regulatory networks by combining gene expression data with protein–protein interaction networks and proteomic datasets. We conclude with a section on practical applications of a network inference algorithm to infer a genome-scale regulatory network.

The authors Alireza Fotuhi Siahpirani and Deborah Chasman contributed equally.

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References

  1. Markowetz F, Spang R (2007) Inferring cellular networks–a review. BMC Bioinf 8(Suppl 6):S5

    Article  CAS  Google Scholar 

  2. Kim HD, Shay T, O’Shea EK, Regev A (2009) Transcriptional regulatory circuits: predicting numbers from alphabets. Science 325(5939):429–432

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Thompson D, Regev A, Roy S (2015) Comparative analysis of gene regulatory networks: from network reconstruction to evolution. Annu Rev Cell Dev Biol 31:399–428

    Article  CAS  PubMed  Google Scholar 

  4. Gasch AP, Spellman PT, Kao CM, Carmel-Harel O, Eisen MB, Storz G, Botstein D, Brown PO (2000) Genomic expression programs in the response of yeast cells to environmental changes. Mol Biol Cell 11(12):4241–4257

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Ideker T, Krogan NJ (2012) Differential network biology. Mol Syst Biol 8:565

    Article  PubMed  PubMed Central  Google Scholar 

  6. Lee TI, Young RA (2013) Transcriptional regulation and its misregulation in disease. Cell 152(6):1237–1251

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Voss TC, Hager GL (2014) Dynamic regulation of transcriptional states by chromatin and transcription factors. Nat Rev Genet 15(2):69–81

    Article  CAS  PubMed  Google Scholar 

  8. de Jong H (2002) Modeling and simulation of genetic regulatory systems: a literature review. J Comput Biol J Comput Mol Cell Biol 9:67–103

    Article  Google Scholar 

  9. Huang S, Kauffman SA (2009) Complex gene regulatory networks – from structure to biological observables: cell fate determination. In: Encyclopedia of complexity and systems science. Springer New York, pp 1180–1213

    Google Scholar 

  10. Carpenter AE, Sabatini DM (2004) Systematic genome-wide screens of gene function. Nat Rev Genet 5(1):11–22

    Article  CAS  PubMed  Google Scholar 

  11. Giaever G, Nislow C (2014) The yeast deletion collection: a decade of functional genomics. Genetics 197(2):451–465

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Ren B, Robert F, Wyrick J, Aparicio O, Jennings E, Simon I, Zeitlinger J, Schreiber J, Hannett N, Kanin E, Volkert T, Wilson C, Bell S, Young R (2000) Genome-wide location and function of DNA binding proteins. Science 290(5500):2306–2309

    Article  CAS  PubMed  Google Scholar 

  13. Furey TS (2012) ChIP-seq and beyond: new and improved methodologies to detect and characterize protein-DNA interactions. Nat Rev Genet 13(12):840–852

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Song L, Crawford GE (2010) DNase-seq: a high-resolution technique for mapping activegene regulatory elements across the genome from mammalian cells. Cold Spring Harb Protoc 2010(2):pdb.prot5384–pdb.prot5384

    Article  Google Scholar 

  15. Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ (2013) Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods 10:1213–1218

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. MacGilvray ME, Shishkova E, Chasman D, Place M, Gitter A, Coon JJ, Gasch AP (2018) Network inference reveals novel connections in pathways regulating growth and defense in the yeast salt response. PLoS Comput Biol 13(5):1–28

    Article  CAS  Google Scholar 

  17. Figeys D (2008) Mapping the human protein interactome. Cell Res 18:716–724

    Article  CAS  PubMed  Google Scholar 

  18. Braun P (2012) Interactome mapping for analysis of complex phenotypes: insights from benchmarking binary interaction assays. Proteomics 12:1499–1518

    Article  CAS  PubMed  Google Scholar 

  19. Friedman N, Nachman I, Peér D (1999) Learning bayesian network structure from massive datasets: The “sparse candidate” algorithm. In: Proceedings of the fifteenth conference on uncertainty in artificial intelligence, UAI’99. Morgan Kaufmann Publishers Inc., San Francisco, CA, pp 206–215

    Google Scholar 

  20. Segal E, Shapira M, Regev A, Pe’er D, Botstein D, Koller D, Friedman N (2003) Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet 34(2):166–176

    Article  CAS  PubMed  Google Scholar 

  21. Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Favera RD, Califano A (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinf 7(Suppl 1):S7+

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  22. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS (2007) Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 5(1):e8+

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  23. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. BMC Bioinf 9:559

    Article  CAS  Google Scholar 

  24. Joshi A, De Smet R, Marchal K, Van de Peer Y, Michoel T (2009) Module networks revisited: computational assessment and prioritization of model predictions. Bioinformatics 25(4):490–496

    Article  CAS  PubMed  Google Scholar 

  25. Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P (2010) Inferring regulatory networks from expression data using Tree-Based methods. PLoS One 5(9):e12776+

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  26. Haury ACC, Mordelet F, Vera-Licona P, Vert JPP (2012) TIGRESS: trustful inference of gene REgulation using stability selection. BMC Syst Biol 6(1):145+

    Article  PubMed  PubMed Central  Google Scholar 

  27. Tibshirani R (1996) Regression shrinkage and selection via the lasso. J R Stat Soc Ser B (Methodol) 58(1):267–288

    Google Scholar 

  28. Meinshausen N, Bühlmann P (2010) Stability selection. J R Stat Soc Ser B (Stat Methodol) 72(4):417–473

    Article  Google Scholar 

  29. Roy S, Lagree S, Hou Z, Thomson JA, Stewart R, Gasch AP (2013) Integrated module and Gene-Specific regulatory inference implicates upstream signaling networks. PLoS Comput Biol 9(10):e1003252+

    Article  PubMed  PubMed Central  Google Scholar 

  30. Marbach D, Costello JC, Küffner R, Vega NM, Prill RJ, Camacho DM, Allison KR, Aderhold A, Allison KR, Bonneau R, et al (2012) Wisdom of crowds for robust gene network inference. Nat Methods 9(8):796–804

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Friedman N (2004) Inferring cellular networks using probabilistic graphical models. Science 303(5659):799–805

    Article  CAS  PubMed  Google Scholar 

  32. De Smet R, Marchal K (2010) Advantages and limitations of current network inference methods. Nat Rev Microbiol 8(10):717–729

    Article  PubMed  CAS  Google Scholar 

  33. Friedman N, Linial M, Nachman I, Pe’er D (2000) Using Bayesian networks to analyze expression data. J Comput Biol 7(3–4):601–620

    Article  CAS  PubMed  Google Scholar 

  34. Pe’er D, Regev A, Tanay A (2002) Minreg: inferring an active regulator set. Bioinformatics (Oxford, England) 18(Suppl 1):S258–S267

    Article  Google Scholar 

  35. Heckerman D, Chickering DM, Meek C, Rounthwaite R, Kadie C (2001) Dependency networks for inference, collaborative filtering, and data visualization. J Mach Learn Res 1:49–75

    Google Scholar 

  36. Werhli AV, Husmeier D (2007) Reconstructing gene regulatory networks with Bayesian networks by combining expression data with multiple sources of prior knowledge. Stat Appl Genet Mol Biol 6(1): Article15

    Google Scholar 

  37. Hill SM, Lu Y, Molina J, Heiser LM, Spellman PT, Speed TP, Gray JW, Mills GB, Mukherjee S (2012) Bayesian inference of signaling network topology in a cancer cell line. Bioinformatics 28(21):2804–2810

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Bonneau R, Reiss D, Shannon P, Facciotti M, Hood L, Baliga N, Thorsson V (2006) The inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol 7(5):R36+

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  39. Greenfield A, Hafemeister C, Bonneau R (2013) Robust data-driven incorporation of prior knowledge into the inference of dynamic regulatory networks. Bioinformatics 29(8):1060–1067

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Koller D, Friedman N (2009) Probabilistic graphical models: principles and techniques. MIT Press, Cambridge, MA

    Google Scholar 

  41. Grzegorczyk M, Husmeier D, Werhli AV (2008) Reverse engineering gene regulatory networks with various machine learning methods. Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim, pp 101–142

    Google Scholar 

  42. Lee SI, Dudley AM, Drubin D, Silver PA, Krogan NJ, Pe’er D, Koller D (2009) Learning a prior on regulatory potential from eQTL data. PLoS Genet 5(1):e1000358

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  43. Siahpirani AF, Roy S (2017) A prior-based integrative framework for functional transcriptional regulatory network inference. Nucleic Acids Res 45:e21

    Article  PubMed  Google Scholar 

  44. Zou H, Hastie T (2005) Regularization and variable selection via the elastic net. J R Stat Soc Ser B Stat Methodol 67(2):301–320

    Article  Google Scholar 

  45. Novershtern N, Regev A, Friedman N (2011) Physical module networks: an integrative approach for reconstructing transcription regulation. Bioinformatics 27(13):i177–i185

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Gitter A, Carmi M, Barkai N, Bar-Joseph Z (2013) Linking the signaling cascades and dynamic regulatory networks controlling stress responses. Genome Res 23(2):365–376

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Gitter A, Bar-Joseph Z (2013) Identifying proteins controlling key disease signaling pathways. Bioinformatics 29(13):i227–i236

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Schulz MH, Devanny WE, Gitter A, Zhong S, Ernst J, Bar-Joseph Z (2012) Drem 2.0: improved reconstruction of dynamic regulatory networks from time-series expression data. BMC Syst Biol 6:104

    Article  PubMed  PubMed Central  Google Scholar 

  49. Chasman D, Walters KB, Lopes TJS, Eisfeld AJ, Kawaoka Y, Roy S (2016) Integrating transcriptomic and proteomic data using predictive regulatory network models of host response to pathogens. PLoS Comput Biol 12:e1005013

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Ernst J, Vainas O, Harbison CT, Simon I, Bar-Joseph Z (2007) Reconstructing dynamic regulatory maps. Mol Syst Biol 3:74

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Gitter A, Klein-Seetharaman J, Gupta A, Bar-Joseph Z (2011) Discovering pathways by orienting edges in protein interaction networks. Nucleic acids Res 39:e22

    Article  PubMed  CAS  Google Scholar 

  52. Bengio Y, Frasconi P (1996) Input-output HMMs for sequence processing. IEEE Trans Neural Netw 7:1231–1249

    Article  CAS  PubMed  Google Scholar 

  53. Yuan M, Lin Y (2006) Model selection and estimation in regression with grouped variables. J R Stat Soc Ser B (Stat Methodol) 68(1):49–67

    Article  Google Scholar 

  54. Obozinski G, Taskar B, Jordan M (2006) Multi-task feature selection, Technical report 2. Statistics Department, UC Berkeley

    Google Scholar 

  55. Ourfali O, Shlomi T, Ideker T, Ruppin E, Sharan R (2007) SPINE: a framework for signaling-regulatory pathway inference from cause-effect experiments. Bioinformatics 23(13):i359–i366

    Article  CAS  PubMed  Google Scholar 

  56. Silverbush D, Elberfeld M, Sharan R (2011) Optimally orienting physical networks. J Comput Biol J Comput Mol Cell Biol 18:1437–1448

    Article  CAS  Google Scholar 

  57. Chasman D, Gancarz B, Hao L, Ferris M, Ahlquist P, Craven M (2014a) Inferring host gene subnetworks involved in viral replication. PLoS Comput Biol 10(5):e1003626

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  58. Chasman D, Ho Y, Berry DB, Nemec CM, MacGilvray ME, Hose J, Merrill AE, Lee MV, Will JL, Coon JJ, Ansari AZ, Craven M, Gasch AP (2014b) Pathway connectivity and signaling coordination in the yeast stress-activated signaling network. Mol Syst Biol 10(11):759+

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  59. Danna E, Fenelon M, Gu Z, Wunderling R (2007) Generating multiple solutions for mixed integer programming problems. In: Integer programming and combinatorial optimization. Springer, Berlin/Heidelberg, pp 280–294

    Chapter  Google Scholar 

  60. Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M (2006) BioGRID: a general repository for interaction datasets. Nucleic Acids Res 34(Suppl 1):D535–D539

    Article  CAS  PubMed  Google Scholar 

  61. Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, Lin J, Minguez P, Bork P, von Mering C, Jensen LJ (2013) String v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res 41(Database issue):D808–D815

    CAS  PubMed  Google Scholar 

  62. Edgar R, Domrachev M, Lash AE (2002) Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res 30(1):207–210

    Article  CAS  PubMed  Google Scholar 

  63. Leinonen R, Sugawara H, Shumway M, Collaboration INSD (2010) The sequence read archive. Nucleic acids Res 39(Suppl 1):D19–D21

    Google Scholar 

  64. Cahan P, Li H, Morris SA, Lummertz da Rocha E, Daley GQ, Collins JJ (2014) Cellnet: network biology applied to stem cell engineering. Cell 158(4):903–915

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Collado-Torres L, Nellore A, Kammers K, Ellis SE, Taub MA, Hansen KD, Jaffe AE, Langmead B, Leek JT (2017) Reproducible RNA-seq analysis using recount2. Nat Biotechnol 35:319–321

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Lachmann A, Torre D, Keenan AB, Jagodnik KM, Lee HJ, Silverstein MC, Wang L, Ma’ayan A (2017) Massive mining of publicly available RNA-seq data from human and mouse. bioRXiv preprint

    Google Scholar 

  67. Bernstein MN, Doan A, Dewey CN (2017) MetaSRA: normalized human sample-specific metadata for the sequence read archive. Bioinformatics (Oxford, England) 33:2914–2923

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Leek JT, Scharpf RB, Bravo HC, Simcha D, Langmead B, Johnson WE, Geman D, Baggerly K, Irizarry RA (2010) Tackling the widespread and critical impact of batch effects in high-throughput data. Nat Rev Genet 11:733–739

    Article  CAS  PubMed  Google Scholar 

  69. Goh WWB, Wang W, Wong L (2017) Why batch effects matter in omics data, and how to avoid them. Trends Biotechnol 35:498–507

    Article  CAS  PubMed  Google Scholar 

  70. Leek JT, Johnson WE, Parker HS, Fertig EJ, Jaffe AE, Storey JD (2015) SVA: Surrogate Variable Analysis. R package version 3.18.0

    Google Scholar 

  71. Johnson WE, Li C, Rabinovic A (2007) Adjusting batch effects in microarray expression data using empirical bayes methods. Biostatistics (Oxford, England) 8:118–127

    Article  PubMed  Google Scholar 

  72. Leek JT, Storey JD (2007) Capturing heterogeneity in gene expression studies by surrogate variable analysis. PLoS Genet 3:1724–1735

    Article  CAS  PubMed  Google Scholar 

  73. Apweiler R, Bairoch A, Wu CH, Barker WC, Boeckmann B, Ferro S, Gasteiger E, Huang H, Lopez R, Magrane M, et al (2004) Uniprot: the universal protein knowledgebase. Nucleic acids Res 32(Suppl 1):D115–D119

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25(1):25–29

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Ravasi T, Suzuki H, Cannistraci CV, Katayama S, Bajic VB, Tan K, Akalin A, Schmeier S, Kanamori-Katayama M, Bertin N, et al (2010) An atlas of combinatorial transcriptional regulation in mouse and man. Cell 140(5):744–752

    Article  CAS  PubMed  Google Scholar 

  76. Jin J, Tian F, Yang DC, Meng YQ, Kong L, Luo J, Gao G (2017) Planttfdb 4.0: toward a central hub for transcription factors and regulatory interactions in plants. Nucleic acids Res 45(D1):D1040–D1045

    Article  CAS  PubMed  Google Scholar 

  77. Mathelier A, Fornes O, Arenillas DJ, Chen CY, Denay G, Lee J, Shi W, Shyr C, Tan G, Worsley-Hunt R, Zhang AW, Parcy F, Lenhard B, Sandelin A, Wasserman WW (2016) Jaspar 2016: a major expansion and update of the open-access database of transcription factor binding profiles. Nucleic Acids Res 44:D110–D115

    Article  CAS  PubMed  Google Scholar 

  78. Weirauch MT, Yang A, Albu M, Cote AG, Montenegro-Montero A, Drewe P, Najafabadi HS, Lambert SA, Mann I, Cook K, Zheng H, Goity A, van Bakel H, Lozano JC, Galli M, Lewsey MG, Huang E, Mukherjee T, Chen X, Reece-Hoyes JS, Govindarajan S, Shaulsky G, Walhout AJM, Bouget FY, Ratsch G, Larrondo LF, Ecker JR, Hughes TR (2014) Determination and inference of eukaryotic transcription factor sequence specificity. Cell 158(6):1431–1443

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Grant CE, Bailey TL, Noble WS (2011) Fimo: scanning for occurrences of a given motif. Bioinformatics 27(7):1017–1018

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS (2008) Model-based analysis of ChiP-Seq (MACS). Genome Biol 9:R137

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  81. Gusmao EG, Allhoff M, Zenke M, Costa IG (2016) Analysis of computational footprinting methods for DNase sequencing experiments. Nat Methods 13(4):303–309

    Article  PubMed  CAS  Google Scholar 

  82. Ritz A, Poirel CL, Tegge AN, Sharp N, Simmons K, Powell A, Kale SD, Murali TM (2016) Pathways on demand: automated reconstruction of human signaling networks. npj Syst Biol Appl 2:16002+

    Google Scholar 

  83. Tuncbag N, Gosline SJC, Kedaigle A, Soltis AR, Gitter A, Fraenkel E (2016) Network-based interpretation of diverse high-throughput datasets through the omics integrator software package. PLOS Comput Biol 12(4):e1004879+

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  84. Almozlino Y, Atias N, Silverbush D, Sharan R (2017) Anat 2.0: reconstructing functional protein subnetworks. BMC Bioinf 18:495

    Google Scholar 

  85. Gurobi Optimization, Inc (2016) Gurobi optimizer reference manual

    Google Scholar 

  86. Davis J, Goadrich M (2006) The relationship between Precision-Recall and ROC curves. In: Proceedings of the 23rd international conference on machine learning (ICML 2006), ICML ’06. ACM, New York, NY, pp 233–240

    Google Scholar 

  87. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45:D353–D361

    Article  CAS  PubMed  Google Scholar 

  88. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP (2011) Molecular signatures database (MSigDB) 3.0. Bioinformatics (Oxford, England) 27:1739–1740

    Google Scholar 

  89. Noble WS (2009) How does multiple testing correction work? Nat Biotechnol 27:1135–1137

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  90. Marbach D, Roy S, Ay F, Meyer PE, Candeias R, Kahveci T, Bristow CA, Kellis M (2012) Predictive regulatory models in drosophila melanogaster by integrative inference of transcriptional networks. Genome Res 22(7):1334–1349

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Bonnet E, Calzone L, Michoel T (2015) Integrative multi-omics module network inference with Lemon-Tree. PLoS Comput Biol 11:e1003983

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  92. Chen S, Witten DM, Shojaie A (2014) Selection and estimation for mixed graphical models. Biometrika https://doi.org/10.1093/biomet/asu051

    Article  PubMed  Google Scholar 

  93. Žitnik M, Zupan B (2015) Gene network inference by fusing data from diverse distributions. Bioinformatics (Oxford, England) 31:i230–i239

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

This work is supported in part by US Environmental Protection Agency grant 83573701, NIH NIGMS grant 1R01GM117339, and NSF CAREER award to Sushmita Roy.

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    Authors and Affiliations

    1. Wisconsin Institute for Discovery, University of Wisconsin-Madison, Madison, WI, USA

      Alireza Fotuhi Siahpirani, Deborah Chasman & Sushmita Roy

    2. Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, USA

      Alireza Fotuhi Siahpirani

    3. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA

      Sushmita Roy

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    1. Alireza Fotuhi Siahpirani
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    2. Deborah Chasman
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    3. Sushmita Roy
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    Correspondence to Sushmita Roy .

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    Editors and Affiliations

    1. School of Informatics, University of Edinburgh, Edinburgh, UK

      Guido Sanguinetti

    2. Department of Electrical Engineering and Computer Science, University of Liège, Liège, Belgium

      Vân Anh Huynh-Thu

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    Siahpirani, A.F., Chasman, D., Roy, S. (2019). Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks. In: Sanguinetti, G., Huynh-Thu, V. (eds) Gene Regulatory Networks. Methods in Molecular Biology, vol 1883. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-8882-2_7

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