1. H. Kishino, P.J. Waddell, Correspondence analysis of genes and tissue types and finding genetic links from microarray data, Genome Inform. 11, 83–95 (2000).
2. J. Schäfer, K. Strimmer, An empirical Bayes approach to inferring large-scale gene association networks, Bioinformatics 21, 754–764 (2005).
3. J. Schäfer, K. Strimmer, A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics, Stat. Appl. Genet. Mol. Biol. 4, Article 32 (2005).
4. L. Glass, and S.A. Kauffman, The logical analysis of continuous non-linear biochemical control networks, J. Theor. Biol. 39, 103–129 (1973).
5. S.A. Kauffman, Metabolic stability and epigenesis in randomly constructed genetic nets, J. Theor. Biol. 22, 437–467 (1969).
6. H. Lähdesmäki, I. Shmulevich, O. Yli-Harja, On learning gene regulatory networks under the Boolean network model, Mach. Learn. 147–167 (2003).
7. I. Shmulevich, E.R. Dougherty, S. Kim, W. Zhang, Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks, Bioinformatics 18(2), 261–274 (2002).
8. N. Friedman, M. Linial, I. Nachman, D. Peer, Using Bayesian networks to analyze expression data, J. Comput. Biol. 7, 601–620 (2000).
9. E. Segal, M. Shapira, A. Regev, D. Pe'er, D. Botstein, D. Koller, N. Friedman, Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data, Nat. Genet. 34, 166–176 (2003).
10. T.S. Gardner, D. di Bernardo, D. Lorenz, J.J. Collins, Inferring genetic networks and identifying compound mode of action via expression profiling, Science 301(5629), 102–105 (2003).
11. J. Tegner, M.K.S. Yeung, J. Hasty, J.J. Collins, Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling, Proc. Natl. Acad. Sci. U.S.A. 100(10), 5944–5949 (2003).
12. A.J. Butte, I.S. Kohane, Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements, Pac. Symp. Biocomput. 5, 415–426 (2000).
13. A.S. Butte, I.S. Kohane, Relevance networks: a first step toward finding genetic regulatory networks within microarray data in The Analysis of Gene Expression Data (eds. G. Parmigiani, E.S. Garett, R.A. Irizarry, S.L. Zeger,), Springer, New York, pp. 428–446, 2003.
14. A. Margolin, I. Nemenman, K. Basso, C. Wiggins, G. Stolovitzky, R. Dalla Favera, A. Califano, ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform. Suppl 1, S7 (2006).
15. J.J. Faith, B. Hayete, J.T. Thaden, I. Mogno, J. Wierzbowski, G. Cottarel, S. Kasif, J.J. Collins, T.S. Gardner, Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles, PLoS Biol. 5(1), e8 (2007).
16. P.E. Meyer, K. Kontos, F. Lafitte, G. Bontempi, Information-theoretic inference of large transcriptional regulatory networks, EUROSIP j. Bioinfor. Syst. Biol. 2007 79879, (2007).
17. J. Kubica, A. Moore, D. Cohn, J. Schneider, cGraph: a fast graph based method for link analysis and queries, Proceedings of IJCAI Text-Mining and Link-Analysis Workshop, Acapulco, Mexico, 2003.
18. M.G. Rabbat, J.R. Treichler, S.L. Wood, M.G. Larimore, Understanding the topology of a telephone network via internally sensed network tomography. Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing, 3, Philadelphia, PA, pp. 977–980, 2005.
19. M.G. Rabbat, M.A.T. Figueiredo, R.D. Nowak, Network inference from co-occurrences, IEEE Trans. Inform. Theory 54(9), 4053–4068 (2008).
20. D. Zhu, M.G. Rabbat, A.O. Hero, R. Nowak, M.A.G. Figueirado, De novo reconstructing signaling pathways from multiple data source, in New Research on Signaling Transduction (B.R. Yanson, ed.), Nova Publisher, New York, 2007.
21. S. Fortunato, Community detection in graphs, Phys. Rep., 486, 75–174 (2010).
22. M.E.J. Newman, Modularity and community structure in networks. PNAS 103(23), 8577–8582 (2006).
23. B.W. Kernighan, S. Lin, An efficient heuristic procedure for partitioning graphs, Bell Sys. Tech. J. 49, 291–307 (1970).
24. U. Luxburg, A tutorial on spectral clustering in Statistics and Computing, 17(4), 395–416 (2007).
25. M. Girvan, M.E.J. Newman, Community structure in social and biological networks, Proc. Nat. Acad. Sci. U.S.A. 99(12), 7821–7826 (2002).
26. G. Palla, I. Derenyi, I. Farkas, T. Vicsek, Uncovering the overlapping community structure of complex networks in nature and society, Nature 435(7043), 814–818 (2005).
27. M. Rosvall, C.T. Bergstrom, Maps of random walks on complex networks reveal community structure, Proc. Nat. Acad. Sci. 105(4), 1118–1123 (2008).
28. F.Y. Wu, The Potts model, Rev. Mod. Phys. 54(1), 235–268 (1982).
29. F.Y. Wu, Potts model and graph theory, J. Stat. Phys. 52(1), 99–112 (1988).
30. M.E.J. Newman, E. Leicht, Mixture models and exploratory analysis in networks. PNAS, 104(23), 9564–9569 (2007).
31. U.N. Raghavan, R. Albert, S. Kumara, Near linear time algorithm to detect community structures in large-scale networks, Phys. Rev. E 76(3), 036106 (2007).
32. D. Zhu, A.O. Hero, Z.S. Qin, A. Swaroop, High throughput screening of co-expressed gene pairs with controlled False Discovery Rate (FDR) and Minimum Acceptable Strength (MAS), J. Comput. Biol. 12(7), 1027–1043 (2005).
33. M.E.J. Newman, M. Girvan, Finding and evaluating community structure in networks, Phys. Rev. E 69(2), 026113 (2004).
34. G. Palla, I. Farkas, P. Pollner, I. Derényi, T. Vicsek, Directed network modules, New J. Phys. 9(6), 186 (2007).
35. M. Rosvall, D. Axelsson, C.T. Bergstrom, The map equation, Eur. Phys. J. Special Top. 178, 13–23 (2009).
36. M. Rosvall, C.T. Bergstrom, Mapping change in large networks, PLoS ONE 5(1), e8694 (2010).
37. D. Marbach, T. Schaffter, C. Mattiussi, D. Floreano, Generating realistic in silico gene networks for performance assessment of reverse engineering methods, J. Comput. Biol. 16(2), 229–239 (2009).
38. D. Marbach, R.J. Prill, T. Schaffter, C. Mattiussi, D. Floreano, G. Stolovitzky, Revealing strengths and weaknesses of methods for gene network inference. Proc. Nat. Acad. Sci. U.S.A. 107(14), 6286–6291 (2010).
39. R.J. Prill, D. Marbach, J. Saez-Rodriguez, P.K. Sorger, L.G. Alexopoulos, X. Xue, N.D. Clarke, G. Altan-Bonnet, G. Stolovitzky, Towards a rigorous assessment of systems biology models: the DREAM3 challenges, PLoS ONE 5(2), e9202 (2010).
40. P. Mendes, Framework for comparative assessment of parameter estimation and inference methods in systems biology, in Learning and Inference in Computational Systems Biology (N.D. Lawrence, M. Girolami, M. Rattray, G. Sanguinetti, eds.), MIT Press, Cambridge, MA, pp. 33–58, 2009.
41. G. Stolovitzky, R.J. Prill, A. Califano, Lessons from the DREAM2 challenges, in Annals of the New York Academy of Sciences (G. Stolovitzky, P. Kahlem, A. Califano, eds.), vol. 1158, pp. 159–195, 2009.
42. B. Alberts, A. Johnson, J. Lewis, M. Raff, K. Roberts, P. Walter, Molecular Biology of the Cell, 4th ed., Garland Publisher 2002.
43. J.-P. Vert, Reconstruction of biological networks by supervised machine learning approaches, in Elements of Computational Systems Biology (H.M. Lodhi, S.H. Muggleton, eds.), John Wiley & Sons, Inc., Hoboken, NJ, 2010.
44. H. Pang A. Lin, M. Holford, B.E. Enerson, B. Lu, M.P. Lawton, E. Floyd, H. Zhao, Pathway analysis using random forests classification and regression, Bioinformatics 22, 2028–2036 (2006).
45. H. Pang, H. Zhao, Building pathway clusters from Random Forests classification using class votes, BMC Bioinform. 9(87) (2008).
46. A. Subramanian, P. Tamayo, V.K. Mootha, S. Mukherjee, B.L. Ebert, M.A. Gillette, A. Paulovich, S.L. Pomeroy, T.R. Golub, E.S. Lander, J.P. Mesirov, Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Nat. Acad. Sci. U.S.A. 102, 15545–15550 (2005).
47. G. Jr. Dennis, B.T. Sherman, D.A. Hosack, J. Yang, W. Gao, H.C. Lane, R.A. Lempicki, DAVID: database for annotation, visualization and integrated discovery. Genome Biol. 4(5), P3 (2003).
48. D.W. Huang, B.T. Sherman, R.A. Lempicki, Systematic and integrative analysis of large gene lists using DAVID Bioinformatics Resources, Nat. Protoc. 4(1), 44–57 (2009).
49. S. Wuchty, E. Ravasz, A. Barabáasi, The architecture of biological networks, in Complex Systems Science in Biomedicine (E. Micheli-Tzanakou, T. Deisboeck, J. Kresh, eds.), Springer US, 2006.
50. A. Zhang, Protein Interaction Networks: Computational Analysis, Cambridge University Press, Cambridge, UK, 2009.
51. K.L. Gunderson, S. Kruglyak, M.S. Graige, F. Garcia, B.G. Kermani, C. Zhao, D. Che, T. Dickinson, E. Wickham, J. Bierle, D. Doucet, M. Milewski, R. Yang, C. Siegmund, J. Haas, L. Zhou, A. Oliphant, J.B. Fan, S. Barnard, M.S. Chee, Decoding randomly ordered DNA arrays, Genome Res. 14, 870–877 (2004).
52. D.J. Lockhart, H. Dong, M.C. Byrne, M.T. Follettie, M.V. Gallo, M.S. Chee, M. Mittmann, C. Wang, M. Kobayashi, H. Horton, E.L. Brown, Expression monitoring by hybridization to high-density oligonucleotide arrays, Nat. Biotechnol. 14, 1675–1680 (1996).
53. M. Schena, D. Shalon, R.W. Davis, P.O. Brown, Quantitative monitoring of gene expression patterns with a complementary DNA microarray, Science 270 (5235), 368–371 (1995).
54. J. Shendure, R.D. Mitra, C. Varma, G.M. Church, Advanced sequencing technologies: methods and goals, Nat. Rev. Genet. 5(5), 335–44 (2004).
55. J. Shendure, H. Ji, Next-generation DNA sequencing, Nat. Biotechnol., 26, 1135–1145 (2008).
56. T. Barrett, D.B. Troup, S.E. Wilhite, P. Ledoux, C. Evangelista, I.F. Kim, M. Tomashevsky, K.A. Marshall, K.H. Phillippy, P.M. Sherman, R.N. Muertter, M. Holko, O. Ayanbule, A. Yefanov, A. Soboleva, NCBI GEO: archive for functional genomics data sets: 10 years on, Nucleic Acids Res. 39, D1005–D1010 (2010) (http://www.ncbi.nlm.nih.gov/geo).
57. H. Parkinson, U. Sarkans, N. Kolesnikov, N. Abeygunawardena, T. Burdett, M. Dylag, I. Emam, A. Farne, E. Hastings, E. Holloway, N. Kurbatova, M. Lukk, J. Malone, R. Mani, E. Pilicheva, G. Rustici, A. Sharma, E. Williams, T. Adamusiak, M. Brandizi, N. Sklyar, A. Brazma, ArrayExpress update: an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic Acids Res. 39, D1002–D1004, (2010) (http://www.ebi.ac.uk/arrayexpress).
58. P.M. Kim, B. Tidor, Subsystem identification through dimensionality reduction of large-scale gene expression data, Genome Res. 13(7), 1706–1718 (2003).
59. S. Huang, Gene expression profiling, genetic networks and cellular states: an integrating concept for tumorigenesis and drug discovery, J. Molec. Med. 77, 469–480 (1999).
60. T. Schlitt, A. Brazma, Modelling in molecular biology: describing transcription regulatory networks at different scales, Philos. Trans. R. Soc. Biol. Sci. 361(1467), 483–494 (2006).
61. D. Heckerman, D. Geiger, M. Chickering, Learning Bayesian networks: The combination of knowledge and statistical data, Mach. Learn. 20, 197–243 (1995).
62. G.F. Cooper, E. Herskovits, A Bayesian method for the induction of probabilistic networks from data, Mach. Learn. 9(4), 309–347 (1992).
63. D.M. Chickering, Optimal structure identification with greedy search, J. Mach. Learn. Res., 3, 507–554 (2002).
64. R.W. Robinson, Counting unlabeled acyclic digraphs, in Combinatorial Mathematics V (C.H.C. Little ed.), Lecture Notes in Mathematics, 622, pp. 28–43, Springer, Berlin, 1977.
65. K. Murphy, Active learning of causal bayes net structure, Technical Report, UC Berkeley, 2001.
66. K. Murphy, Bayes Net Toolbox v5 for MATLAB, MIT AI Lab, Cambridge, MA, 2003.
67. V. Driessche, J. Demsar, E.O. Booth, P. Hill, P. Juvan, B. Zupan, A. Kuspa, G. Shaulsky, Epistasis analysis with global transcriptional phenotypes, Nat. Genet. 37(5), 471–477 (2005).
68. D. Zhu, M.L. Dequéant, H. Li, Comparative analysis of distance based clustering methods, in Analysis of Microarray Data: A Network Based Approach, (F. Emmert-Streib, M. Dehmer, ed.), Wiley-VCH, Weinheim, Germany, 2007.
69. G. Altay, F. Emmert-Streib, Revealing differences in gene network inference algorithms on the network level by ensemble methods, Bioinformatics 26(14), 1738–1744 (2010).
70. P.E. Meyer, F. Lafitte, G. Bontempi, Minet: an open source R/Bioconductor package for mutual information based network inference, BMC Bioinform. 9, 461 (2008).
71. F. Emmert-Streib, G. Altay, Local network-based measures to assess the inferability of different regulatory networks, IET Syst. Biol. 4(4), 277–288 (2010).
72. Y. Benjamini, Y. Hochberg, Controlling the false discovery rate: a practical and powerful approach to multiple testing, J. R. Stat. Soc. Ser. B. 57(1), 289–300 (1995).
73. Y. Benjamini, D. Yekutieli, False discovery rate adjusted multiple confidence intervals for selected parameters, J. Am. Stat. Assoc. 100, 71–80 (2004).
74. O. Ledoit, M. Wolf, Improved estimation of the covariance matrix of stock returns with an application to portfolio selection, J. Emp. Finance 10, 603–621 (2003).
75. M. Kanehisa, S. Goto, M. Hattori, K.F. Aoki-Kinoshita, M. Itoh, S. Kawashima, T. Katayama, M. Araki, M. Hirakawa, From genomics to chemical genomics: new developments in KEGG, Nucleic Acids Res. 34 (Database issue), D354–D357 (2006).
76. W.W. Zachary, An information flow model for conflict and fission in small groups, J. Anthropol. Res. 33, 452–473 (1977).
77. L.C. Freeman, A set of measures of centrality based on betweenness, Sociometry 40, 35–41 (1977).
78. B. Adamcsek, G. Palla, I.J. Farkas, I. Derenyi, T. Vicsek, CFinder: locating cliques and overlapping modules in biological networks, Bioinformatics 22(8), 1021–1023 (2006).
79. E. Leicht, M.E.J. Newman, Community structure in directed networks, Phys. Rev. Lett. 100(11), 118703 (2008).
80. Y. Kim, S. Son, H. Jeong, Finding communities in directed networks. Phys. Rev. E 81(1), 016103 (2010).
81. P. Dunne, The Complexity of Boolean Networks, Academic Press, CA, 1988.
82. R.J. Tocci, R.S. Widmer, Digital Systems: Principles and Applications, 8 edn., Prentice Hall, NJ, 2001.