On The Reconstruction of Interaction Networks with Applications to Transcriptional Regulation

A novel information-theoretic method for reconstruction of interaction networks is introduced. We prove that the method is exact for some class of networks. Performance tests on large synthetic transcriptional regulatory networks produce very encouraging results.

[1]  Pedro Mendes,et al.  Artificial gene networks for objective comparison of analysis algorithms , 2003, ECCB.

[2]  Adam A. Margolin,et al.  Reverse engineering of regulatory networks in human B cells , 2005, Nature Genetics.

[3]  M. Mézard,et al.  The Bethe lattice spin glass revisited , 2000, cond-mat/0009418.

[4]  Alan M. Frieze,et al.  Random graphs , 2006, SODA '06.

[5]  L. Györfi,et al.  Nonparametric entropy estimation. An overview , 1997 .

[6]  Martin A. Nowak,et al.  Inferring Cellular Networks Using Probabilistic Graphical Models , 2004 .

[7]  M. Reinders,et al.  Genetic network modeling. , 2002, Pharmacogenomics.

[8]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[9]  E. M.,et al.  Statistical Mechanics , 2021, Manual for Theoretical Chemistry.

[10]  R. Kikuchi A Theory of Cooperative Phenomena , 1951 .

[11]  J. Collins,et al.  Inferring Genetic Networks and Identifying Compound Mode of Action via Expression Profiling , 2003, Science.

[12]  S. Mangan,et al.  Structure and function of the feed-forward loop network motif , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[13]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[14]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[15]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[16]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[17]  H. Bethe Statistical Theory of Superlattices , 1935 .

[18]  J. Yedidia An Idiosyncratic Journey Beyond Mean Field Theory , 2000 .

[19]  C. N. Liu,et al.  Approximating discrete probability distributions with dependence trees , 1968, IEEE Trans. Inf. Theory.

[20]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

[21]  I S Kohane,et al.  Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[22]  Ilya Nemenman Information theory, multivariate dependence, and genetic network inference , 2004, ArXiv.