Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Favera RD, Califano A. Aracne: An algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics. 2006:7(Suppl 1).
Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS. Large-scale mapping and validation of escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 2007; 5(1):8.
Article
Google Scholar
Friedman N, Linial M, Nachman I, Pe’er D. Using Bayesian networks to analyze expression data. In: International Conference on Computational Molecular Biology. New York: Mary Ann Liebert, Inc.: 2000. p. 601–20.
Google Scholar
Affeldt S, Verny L, Isambert H. 3off2: A network reconstruction algorithm based on 2-point and 3-point information statistics. BMC Bioinformatics. 2016; 17(S-2):12.
Article
PubMed
PubMed Central
Google Scholar
Marbach D, Costello JC, Küffner R, Vega NM, Prill RJ, Camacho DM, Allison KR, Kellis M, Collins JJ, Stolovitzky G, et al. Wisdom of crowds for robust gene network inference. Nat Methods. 2012; 9(8):796–804.
Article
CAS
PubMed
PubMed Central
Google Scholar
Smet RD, Marchal K. Advantages and limitations of current network inference methods. Nat Rev Micro. 2010; 10(8):717–29.
Google Scholar
Bellot P, Olsen C, Salembier P, Oliveras A, Meyer PE. Netbenchmark: a bioconductor package for reproducible benchmarks of gene regulatory network inference. BMC Bioinformatics. 2015; 16(312):1–15.
Google Scholar
Marbach D, Prill RJ, Schaffter T, Mattiussi C, Floreano D, Stolovitzky G. Revealing strengths and weaknesses of methods for gene network inference. Proc Natl Acad Sci. 2010; 107(14):6286–291.
Article
CAS
PubMed
PubMed Central
Google Scholar
Pearl J, Verma TS. A theory of inferred causation. Studies in Logic and the Foundations of Mathematics. 1995; 134:789–811.
Article
Google Scholar
Spirtes P, Glymour C. An algorithm for fast recovery of sparse causal graphs. Soc Sci Comput Rev. 1991; 9:62–72.
Article
Google Scholar
Cooper GF, Herskovits E. A bayesian method for the induction of probabilistic networks from data. Mach Learn. 1992; 9(4):309–47.
Google Scholar
Heckerman D, Geiger D, Chickering DM. Learning bayesian networks: the combination of knowledge and statistical data. Mach Learn. 1995; 20(3):197–243.
Google Scholar
Cano A, Gomez-Olmedo M, Moral S. A score based ranking of the edges for the PC algorithm. In: Proceedings of the Fourth European Workshop on Probabilistic Graphical Models: 2008. p. 41–8.
de Campos L. A scoring function for learning bayesian networks based on mutual information and conditional independence tests. J Mach Learn Res. 2006; 7:2149–187.
Google Scholar
Tsamardinos I, Brown L, Aliferis CF. The max-min hill-climbing bayesian network structure learning algorithm. Mach Learn. 2006; 65(1):31–78.
Article
Google Scholar
Fiedler M. A property of eigenvectors of nonnegative symmetric matrices and its application to graph theory. Czechoslov Math J. 1975; 25(100):619–33.
Google Scholar
Fiedler M. Algebraic connectivity of graphs. Czechoslov Math J. 1973; 23(98):298–305.
Google Scholar
Shi J, Malik J. Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell. 2000; 22(8):888–905.
Article
Google Scholar
Spielman DA, Teng SH. Spectral partitioning works: Planar graphs and finite element meshes. In: Foundations of Computer Science, 1996. Proceedings., 37th Annual Symposium on. IEEE: 1996. p. 96–105.
Newman MEJ. Modularity and community structure in networks. Proc Natl Acad Sci. 2006; 103(23):8577–82.
Article
CAS
PubMed
PubMed Central
Google Scholar
Hotelling H. Analysis of a complex of statistical variables into principal components. J Educ Psych. 1933; 24:417–41.
Article
Google Scholar
Kruskal JB, Wish M. Multidimensional scaling. Beverely Hills: Sage Publications; 1978.
Book
Google Scholar
Schölkopf B, Smola A, Müller KR. Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput. 1998; 10(5):1299–319.
Article
Google Scholar
Azar Y, Fiat A, Karlin A, McSherry F, Saia J. Spectral analysis of data. In: Proceedings of the thirty-third annual ACM symposium on Theory of computing. ACM: 2001. p. 619–626.
Kannan R, Vempala S, Vetta A. On clusterings: Good, bad and spectral. J ACM. 2004; 51(3):497–515.
Article
Google Scholar
Perona P, Freeman WT. A factorization approach to grouping. In: European Conference on Computer Vision. Springer: 1998. p. 655–70.
Alpert C, Kahng A, Yao S. Spectral partitioning: the more eigenvectors, the better. Discrete Appl Math. 1999; 90:3–26.
Article
Google Scholar
Ng AY, Jordan MI, Weiss Y. On Spectral Clustering: Analysis and an algorithm. In: Advances in Neural Information Processing Systems. MIT Press: 2001. p. 849–56.
Brand M, Huang K. A Unifying Theorem for Spectral Embedding and Clustering. In: Proc. 9th International Workshop on AI and Statistics: 2003. http://www.merl.com/publications/TR2002-042/.
Herrgård MJ, Swainston N, Dobson P, Dunn WB, Arga KY, Arvas M, Blüthgen N, Borger S, Costenoble R, Heinemann M, et al. A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology. Nat Biotechnol. 2008; 26(10):1155–60.
Article
PubMed
PubMed Central
Google Scholar
Fröehlich H, Klau GW. Reconstructing consensus bayesian network structures with application to learning molecular interaction networks. In: GCB. Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik: 2013. p. 46–55. http://dx.doi.org/10.4230/OASIcs.GCB.2013.46.
Berto S, Perdomo-Sabogal A, Gerighausen D, Qin J, Nowick K. A consensus network of gene regulatory factors in the human frontal lobe. Front Genet. 2016; 7.
Lancichinetti A, Fortunato S. Consensus clustering in complex networks. Scientific reports. 2012; 2.
Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, et al. A human gut microbial gene catalogue established by metagenomic sequencing. Nature. 2010; 464(7285):59–65.
Article
CAS
PubMed
PubMed Central
Google Scholar
Nicholson JK, Holmes E, Kinross J, Burcelin R, Gibson G, Jia W, Pettersson S. Host-gut microbiota metabolic interactions. Science. 2012; 336(6086):1262–7.
Article
CAS
PubMed
Google Scholar
Lozupone CA, Stombaugh JI, Gordon JI, Jansson JK, Knight R. Diversity, stability and resilience of the human gut microbiota. Nature. 2012; 489(7415):220–30.
Article
CAS
PubMed
PubMed Central
Google Scholar
Walsh CJ, Guinane CM, O’Toole PW, Cotter PD. Beneficial modulation of the gut microbiota. FEBS Lett. 2014; 588(22):4120–30.
Article
CAS
PubMed
Google Scholar
Clarke SF, Murphy EF, Nilaweera K, Ross PR, Shanahan F, O’Toole PW, Cotter PD. The gut microbiota and its relationship to diet and obesity: new insights. Gut Microbes. 2012; 3(3):186–202.
Article
PubMed
PubMed Central
Google Scholar
Ley RE, Turnbaugh PJ, Klein S, Gordon JI. Microbial ecology: human gut microbes associated with obesity. Nature. 2006; 444(7122):1022–3.
Article
CAS
PubMed
Google Scholar
Elson CO, Cong Y. Host-microbiota interactions in inflammatory bowel disease. Gut Microbes. 2012; 3(4):332–44.
Article
PubMed
PubMed Central
Google Scholar
Lepage P, Häsler R, Spehlmann ME, Rehman A, Zvirbliene A, Begun A, Ott S, Kupcinskas L, Doré J, Raedler A, et al. Twin study indicates loss of interaction between microbiota and mucosa of patients with ulcerative colitis. Gastroenterology. 2011; 141(1):227–36.
Article
PubMed
Google Scholar
Bajaj JS, Thacker LR, Heuman DM, Fuchs M, Sterling RK, Sanyal AJ, Puri P, Siddiqui MS, Stravitz RT, Bouneva I, et al. The stroop smartphone application is a short and valid method to screen for minimal hepatic encephalopathy. Hepatology. 2013; 58(3):1122–32.
Article
PubMed
PubMed Central
Google Scholar
Qin N, Yang F, Li A, Prifti E, Chen Y, Shao L, Guo J, Le Chatelier E, Yao J, Wu L, et al. Alterations of the human gut microbiome in liver cirrhosis. Nature. 2014; 513(7516):59–64.
Article
CAS
PubMed
Google Scholar
Wen L, Ley RE, Volchkov PY, Stranges PB, Avanesyan L, Stonebraker AC, Hu C, Wong FS, Szot GL, Bluestone JA, et al. Innate immunity and intestinal microbiota in the development of type 1 diabetes. Nature. 2008; 455(7216):1109–13.
Article
CAS
PubMed
PubMed Central
Google Scholar
Karlsson FH, Tremaroli V, Nookaew I, Bergström G, Behre CJ, Fagerberg B, Nielsen J, Bäckhed F. Gut metagenome in european women with normal, impaired and diabetic glucose control. Nature. 2013; 498(7452):99–103.
Article
CAS
PubMed
Google Scholar
Qin J, Li Y, Cai Z, Li S, Zhu J, Zhang F, Liang S, Zhang W, Guan Y, Shen D, et al. A metagenome-wide association study of gut microbiota in type 2 diabetes. Nature. 2012; 490(7418):55–60.
Article
CAS
PubMed
Google Scholar
Nielsen HB, Almeida M, Juncker AS, Rasmussen S, Li J, Sunagawa S, Plichta DR, Gautier L, Pedersen AG, Le Chatelier E, et al. Identification and assembly of genomes and genetic elements in complex metagenomic samples without using reference genomes. Nat Biotechnol. 2014; 32(8):822–8.
Article
CAS
PubMed
Google Scholar
Faust K, Raes J. Microbial interactions: from networks to models. Nat Rev Microbiol. 2012; 10(8):538–50.
Article
CAS
PubMed
Google Scholar
Mohar B, Alavi Y, Chartrand G, Oellermann O. The laplacian spectrum of graphs. Graph Theory Comb Appl. 1991; 2(871-898):12.
Google Scholar
Luxburg U. A tutorial on spectral clustering. Stat Comput. 2007; 17(4):395–416.
Article
Google Scholar
Newman MEJ. Finding community structure in networks using the eigenvectors of matrices. Phys Rev E. 2006; 74(3).
Golub GH, van Loan CF. Matrix Computations. Johns Hopkins Series in the Mathematical Sciences. Favoritenstrasse: The Johns Hopkins University Press; 1989.
Google Scholar
Pothen A, Simon HD, Liu K. -P. P. Partitioning sparse matrices with eigenvectors of graphs. Technical report NASA Ames Research Center. 1989.
Kleinberg JM. Authoritative sources in a hyperlinked environment. J ACM (JACM). 1999; 46(5):604–32.
Article
Google Scholar
Miller B, Bliss N, Wolfe PJ. Subgraph detection using eigenvector L1 norms. In: NIPS: 2010. p. 1633–1641. http://www.bibsonomy.org/bibtex/22fa92e5556307d62c4ed6473f4bba10c/dblp.
Russakoff DB, Tomasi C, Rohlfing T, Jr CRM. Image similarity using mutual information of regions. In: 8th European Conference on Computer Vision (ECCV. Springer: 2004. p. 596–607.
Liu R, Gillies DF. An eigenvalue-problem formulation for non-parametric mutual information maximisation for linear dimensionality reduction. In: International Conference on Image Processing, Computer Vision, and Pattern Recognition: 2012. p. 905–910.
Priness I, Maimon O, Ben-Gal IE. Evaluation of gene-expression clustering via mutual information distance measure. BMC Bioinformatics. 2007; 8.
Steuer R, Kurths J, Daub CO, Weise J, Selbig J. The mutual information: Detecting and evaluating dependencies between variables. Bioinformatics. 2002; 18:231–40.
Article
Google Scholar
Butte AJ, Kohane IS, Kohane IS. Mutual information relevance networks: Functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput. 2000; 5:415–26.
Google Scholar
Scutari M, Denis JB. Bayesian Networks with Examples in R. Boca Raton: Chapman and Hall; 2014.
Google Scholar
Conati C, Gertner AS, VanLehn K, Druzdzel MJ. On-line student modeling for coached problem solving using Bayesian networks. In: User Modeling. Springer: 1997. p. 231–42.
Andreassen S, Jensen F, Andersen S, Falck B, Kjærulff U, Woldbye M, Sørensen A, Rosenfalck A, Jensen F. MUNIN - an Expert EMG Assistant. In: Computer-Aided Electromyography and Expert Systems, Chapter 21. Noth-Holland: Elsevier: 1989.
Google Scholar
Le Chatelier E, Nielsen T, Qin J, Prifti E, Hildebrand F, Falony G, Almeida M, Arumugam M, Batto JM, Kennedy S, et al. Richness of human gut microbiome correlates with metabolic markers. Nature. 2013; 500(7464):541–6.
Article
CAS
PubMed
Google Scholar
Sales G, Romualdi C. parmigene-a parallel r package for mutual information estimation and gene network reconstruction. Bioinformatics. 2011; 27(13):1876–7.
Article
CAS
PubMed
Google Scholar
Qiu P, Gentles AJ, Plevritis SK. Fast calculation of pairwise mutual information for gene regulatory network reconstruction. Comput Methods Prog Biomed. 2009; 94(2):177–80.
Article
Google Scholar