Biological Network Inference at Multiple Scales: From Gene Regulation to Species Interactions

This article describes how a unifying approach based on machine learning enables inference of gene regulatory networks from post-genomic data as well as ecological networks from species abundance data.

[1]  Bartek Wilczynski,et al.  Active enhancer positions can be accurately predicted from chromatin marks and collective sequence motif data , 2013, BMC Systems Biology.

[2]  Dirk Husmeier,et al.  Reconstructing ecological networks with hierarchical Bayesian regression and Mondrian processes , 2013, AISTATS.

[3]  M. Grzegorczyk,et al.  Regularization of non-homogeneous dynamic Bayesian networks with global information-coupling based on hierarchical Bayesian models , 2013, Machine Learning.

[4]  Andrew J. Millar,et al.  Modelling the widespread effects of TOC1 signalling on the plant circadian clock and its outputs , 2013, BMC Systems Biology.

[5]  Ping Ao,et al.  From Phage lambda to human cancer: endogenous molecular-cellular network hypothesis , 2013, Quantitative Biology.

[6]  Atina G. Coté,et al.  Evaluation of methods for modeling transcription factor sequence specificity , 2013, Nature Biotechnology.

[7]  Dirk Husmeier,et al.  Hierarchical Bayesian models in ecology: Reconstructing species interaction networks from non-homogeneous species abundance data , 2012, Ecol. Informatics.

[8]  J. Hillston,et al.  Stochastic properties of the plant circadian clock , 2012, Journal of The Royal Society Interface.

[9]  A. Millar,et al.  The clock gene circuit in Arabidopsis includes a repressilator with additional feedback loops , 2012, Molecular systems biology.

[10]  Colin M. Beale,et al.  Are richness patterns of common and rare species equally well explained by environmental variables , 2011 .

[11]  Paul E. Brown,et al.  Quantitative analysis of regulatory flexibility under changing environmental conditions , 2010, Molecular systems biology.

[12]  Dirk Husmeier,et al.  Inferring species interaction networks from species abundance data: A comparative evaluation of various statistical and machine learning methods , 2010, Ecol. Informatics.

[13]  Michael P. H. Stumpf,et al.  Statistical inference of the time-varying structure of gene-regulation networks , 2010, BMC Systems Biology.

[14]  Takeshi Mizuno,et al.  Data assimilation constrains new connections and components in a complex, eukaryotic circadian clock model , 2010, Molecular Systems Biology.

[15]  P. Cox,et al.  Estimating Amazonian rainforest stability and the likelihood for large-scale forest dieback , 2010 .

[16]  Anil Wipat,et al.  Feedback between p21 and reactive oxygen production is necessary for cell senescence , 2010, Molecular systems biology.

[17]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[18]  Jane Hillston,et al.  Bio-PEPA: A framework for the modelling and analysis of biological systems , 2009, Theor. Comput. Sci..

[19]  Mark A. Girolami,et al.  Bayesian ranking of biochemical system models , 2008, Bioinform..

[20]  J. Collins,et al.  Size matters: network inference tackles the genome scale , 2007, Molecular systems biology.

[21]  Marco Grzegorczyk,et al.  Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks , 2006, Bioinform..

[22]  Mark Goadrich,et al.  The relationship between Precision-Recall and ROC curves , 2006, ICML.

[23]  Christoph Flamm,et al.  The expansion of the metazoan microRNA repertoire , 2006, BMC Genomics.

[24]  Paul E. Brown,et al.  Extension of a genetic network model by iterative experimentation and mathematical analysis , 2005, Molecular systems biology.

[25]  K. Sachs,et al.  Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data , 2005, Science.

[26]  H. Zou,et al.  Regularization and variable selection via the elastic net , 2005 .

[27]  Joel E. Cohen,et al.  Mathematics Is Biology's Next Microscope, Only Better; Biology Is Mathematics' Next Physics, Only Better , 2004, PLoS biology.

[28]  Dirk Husmeier,et al.  Sensitivity and specificity of inferring genetic regulatory interactions from microarray experiments with dynamic Bayesian networks , 2003, Bioinform..

[29]  K. Shinozaki,et al.  Regulatory network of gene expression in the drought and cold stress responses. , 2003, Current opinion in plant biology.

[30]  D. Haydon,et al.  Alternative stable states in ecology , 2003 .

[31]  B. S. Baker,et al.  Gene Expression During the Life Cycle of Drosophila melanogaster , 2002, Science.

[32]  Neo D. Martinez,et al.  Network structure and biodiversity loss in food webs: robustness increases with connectance , 2002, Ecology Letters.

[33]  S. Carpenter,et al.  Catastrophic shifts in ecosystems , 2001, Nature.

[34]  J Memmott,et al.  Infiltration of a Hawaiian Community by Introduced Biological Control Agents , 2001, Science.

[35]  Patrik D'haeseleer,et al.  Genetic network inference: from co-expression clustering to reverse engineering , 2000, Bioinform..

[36]  Michal Linial,et al.  Using Bayesian networks to analyze expression data , 2000, RECOMB '00.

[37]  R. B. Jackson,et al.  Global biodiversity scenarios for the year 2100. , 2000, Science.

[38]  Neo D. Martinez,et al.  Simple rules yield complex food webs , 2000, Nature.

[39]  Jack J. Lennon,et al.  Red-shifts and red herrings in geographical ecology , 2000 .

[40]  Christophe Andrieu,et al.  Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC , 1999, IEEE Trans. Signal Process..

[41]  Terry V. Callaghan,et al.  The balance between positive and negative plant interactions and its relationship to environmental gradients : a model , 1998 .

[42]  P. Green Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[43]  Ronald W. Davis,et al.  Quantitative Monitoring of Gene Expression Patterns with a Complementary DNA Microarray , 1995, Science.

[44]  Joel E. Cohen,et al.  A food web approach to evaluating the effect of insecticide spraying on insect pest population dynamics in a Philippine irrigated rice ecosystem , 1994 .

[45]  D. Rubin,et al.  Inference from Iterative Simulation Using Multiple Sequences , 1992 .

[46]  J. Hanley,et al.  The meaning and use of the area under a receiver operating characteristic (ROC) curve. , 1982, Radiology.

[47]  D. Gillespie Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .

[48]  V. Smith,et al.  ASSESSMENT OF REGRESSION METHODS FOR INFERENCE OF REGULATORY NETWORKS INVOLVED IN CIRCADIAN REGULATION , 2013 .

[49]  Daniel M. Roy Computability, inference and modeling in probabilistic programming , 2011 .

[50]  Christine L. Mumford,et al.  Synergy in computational intelligence , 2009 .

[51]  A. Sinclair,et al.  Understanding ecosystem dynamics for conservation of biota. , 2006, The Journal of animal ecology.

[52]  R. Tait The application of molecular biology. , 1999, Current issues in molecular biology.