Netter: Re-ranking gene network inference predictions using structural network properties

BMC Bioinformatics

Volumn 17, Issue 1, 2016, Pages

  Ruyssinck, Joeri ^a   Demeester, Piet ^a   Dhaene, Tom ^a   Saeys, Yvan ^a,b  

^a GHENT UNIVERSITY   (Belgium)

^b DEPARTMENT OF PLANT SYSTEMS BIOLOGY   (Belgium)

Author keywords

Gene expression data; Gene regulatory networks; Graphlets; Network inference

Indexed keywords

ALGORITHMS; FORECASTING; GENES; INFERENCE ENGINES; OPTIMIZATION; TOPOLOGY;

GENE EXPRESSION DATA; GENE REGULATORY NETWORKS; GRAPHLETS; NETWORK INFERENCE; POSTPROCESSING ALGORITHMS; REGULATORY INTERACTIONS; STATE-OF-THE-ART ALGORITHMS; STRUCTURAL NETWORK PROPERTIES;

GENE EXPRESSION;

BIOINFORMATICS; DREAM; ESCHERICHIA COLI; HUMAN; PREDICTION; ALGORITHM; BENCHMARKING; BIOLOGY; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORK; GENETICS; PROCEDURES;

ALGORITHMS; BENCHMARKING; COMPUTATIONAL BIOLOGY; ESCHERICHIA COLI; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORKS; HUMANS;

EID: 84960085965     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-016-0913-0     Document Type: Article

References (34)

1. Narendra V, Lytkin NI, Aliferis CF, Statnikov A. A comprehensive assessment of methods for de-novo reverse-engineering of genome-scale regulatory networks. Genomics. 2011; 97(1):7-18.
2. Soranzo N, Bianconi G, Altafini C. Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data. Bioinformatics. 2007; 23(13):1640-1647.
3. Hache H, Lehrach H, Herwig R. Reverse engineering of gene regulatory networks: a comparative study. EURASIP J Bioinformatics Syst Biol. 2009; 2009:8-1812.
4. De Smet R, Marchal K. Advantages and limitations of current network inference methods. Nat Rev Micro. 2010; 8(10):717-29.
5. Michoel T, De Smet R, Joshi A, Van de Peer Y, Marchal K. Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks. BMC Systems Biology. 2009; 3(1):49.
6. Marbach D, Costello J, Kuffner R, Vega N, Prill R, Camacho D, et al.Wisdom of crowds for robust gene network inference. Nat Meth. 2012; 9(8):796-804.
7. Reich M, Liefeld T, Gould J, Lerner J, Tamayo P, Mesirov JP. GenePattern 2.0. Nat. Genet. 2006; 38(5):500-1.
8. Küffner R, Petri T, Tavakkolkhah P, Windhager L, Zimmer R. Inferring gene regulatory networks by ANOVA. Bioinforma. 2012; 28(10):1376-1382.
9. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, et al.Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles. PLoS Biol. 2007; 5(1):8.
10. Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P. Inferring Regulatory Networks from Expression Data Using Tree-Based Methods. PLoS ONE. 2010; 5(9):12776.
11. Bonneau R, Reiss DJ, Shannon P, Facciotti M, Hood L, Baliga NS, et al.The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biology. 2006; 7(5):36.
12. Haury AC, Mordelet F, Vera-Licona P, Vert JP. TIGRESS: Trustful Inference of Gene REgulation using Stability Selection. BMC Syst Biol. 2012; 6(1):145.
13. Butte AJ, Kohane IS. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput. 2000; 5:415-26.
14. Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Favera RD, et al.ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context. BMC Bioinforma. 2006; 7(Suppl 1):7-7.
15. Langfelder P, Horvath S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinforma. 2008; 9:559.
16. Feizi S, Marbach D, Médard M, Kellis M. Network deconvolution as a general method to distinguish direct dependencies in networks. Nat Biotechnol. 2013; 31(8):726-33.
17. Barzel B, Barabási AL. Network link prediction by global silencing of indirect correlations. Nat Biotechnol. 2013; 31(8):720-5.
18. Barabási AL, Oltvai ZN. Network biology: understanding the cell's functional organization. Nat Rev Genet. 2004; 5(2):101-13.
19. Grigorov MG. Global properties of biological networks. Drug Discovery Today. 2005; 10(5):365-72.
20. Luscombe NM, Babu MM, Yu H, Snyder M, Teichmann SA, Gerstein M. Genomic analysis of regulatory network dynamics reveals large topological changes. Nature. 2004; 431(7006):308-12.
21. Albert R. Scale-free networks in cell biology. J Cell Science. 2005; 118:4947-957.
22. Schlitt T, Brazma A. Current approaches to gene regulatory network modelling. BMC Bioinforma. 2007; 8(Suppl 6):9.
23. Alon U. Network motifs: theory and experimental approaches. Nat Rev Genet. 2007; 8(6):450-61.
24. Kirkpatrick S, Gelatt CD, Vecchi MP. Optimization by Simulated Annealing. Science. 1983; 220(4598):671-80.
25. Pržulj N, Corneil DG, Jurisica I. Modeling interactome: scale-free or geometric?Bioinforma. 2004; 20(18):3508-515.
26. Pržulj N. Biological network comparison using graphlet degree distribution. Bioinforma. 2007; 23(2):177-83.
27. 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.
28. de Matos Simoes R, Emmert-Streib F. Bagging statistical network inference from large-scale gene expression data. PLoS ONE. 2012; 7(3):33624.
29. Ruyssinck J, Huynh-Thu VA, Geurts P, Dhaene T, Demeester P, Saeys Y. Nimefi: Gene regulatory network inference using multiple ensemble feature importance algorithms. PLoS ONE. 2014; 9(3):92709.
30. Marbach D, Schaffter T, Mattiussi C, Floreano D. Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods. J Comput Biol. 2009; 16(2):229-39.
31. Prill RJ, Marbach D, Saez-Rodriguez J, Sorger PK, Alexopoulos LG, Xue X, et al.Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges. PLoS ONE. 2010; 5(2):9202.
32. Van den Bulcke T, Van Leemput K, Naudts B, van Remortel P, Ma H, Verschoren A, et al.SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. BMC Bioinforma. 2006; 7(1):43.
33. Schaffter T, Marbach D, Floreano D. GeneNetWeaver: In silico benchmark generation and performance profiling of network inference methods. Bioinforma. 2011; 27(16):2263-270.
34. Bastiaens P, Birtwistle MR, Bluthgen N, Bruggeman FJ, Cho KH, Cosentino C, et al.Silence on the relevant literature and errors in implementation. Nat Biotech. 2015; 33(4):336-9.