A comprehensive assessment of methods for de-novo reverse-engineering of genome-scale regulatory networks

Genomics

Volumn 97, Issue 1, 2011, Pages 7-18

  Narendra, Varun ^a   Lytkin, Nikita I ^a   Aliferis, Constantin F ^a,b   Statnikov, Alexander ^a  

^a NEW YORK UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b VANDERBILT UNIVERSITY   (United States)

Author keywords

Computational methods; Evaluation; Gene expression microarray analysis; Regulatory network de novo reverse engineering

Indexed keywords

ALGORITHM; ARTICLE; AUTOMATION; DATA ANALYSIS; DE NOVO REVERSE ENGINEERING; ENGINEERING; GENE REGULATORY NETWORK; METHODOLOGY; PRIORITY JOURNAL; STATISTICAL ANALYSIS;

ALGORITHMS; COMPUTATIONAL BIOLOGY; DATABASES, NUCLEIC ACID; GENE REGULATORY NETWORKS; GENOME; METHODS; MULTIVARIATE ANALYSIS;

PYRONIA TITHONUS;

EID: 78650773077     PISSN: 08887543     EISSN: 10898646     Source Type: Journal    
DOI: 10.1016/j.ygeno.2010.10.003     Document Type: Article

References (48)

1. Adriaens M.E., Jaillard M., Waagmeester A., Coort S.L., Pico A.R., Evelo C.T. The public road to high-quality curated biological pathways. Drug Discov. Today 2008, 13:856-862.
2. Aliferis C.F., Statnikov A., Tsamardinos I., Mani S., Koutsoukos X.D. Local causal and Markov blanket induction for causal discovery and feature selection for classification: Part I. Algorithms and empirical evaluation. J. Mach. Learn. Res. 2010, 11:171-234.
3. Aliferis C.F., Statnikov A., Tsamardinos I., Mani S., Koutsoukos X.D. Local causal and Markov blanket induction for causal discovery and feature selection for classification: Part II. Analysis and extensions. J. Mach. Learn. Res. 2010, 11:235-284.
4. Anderson T.W. An Introduction to Multivariate Statistical Analysis 2003, Wiley-Interscience, Hoboken, NJ.
5. Bach F.R., Jordan M.I. Learning graphical models with Mercer kernels. Adv. Neural Inf. Process. Syst. (NIPS) 2003, 15:1009-1016.
6. Bansal M., Belcastro V., Ambesi-Impiombato A., di B.D. How to infer gene networks from expression profiles. Mol. Syst. Biol. 2007, 3:78.
7. Barrett T., Troup D.B., Wilhite S.E., Ledoux P., Rudnev D., Evangelista C., Kim I.F., Soboleva A., Tomashevsky M., Marshall K.A., Phillippy K.H., Sherman P.M., Muertter R.N., Edgar R. NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res. 2009, 37:D885-D890.
8. Basso K., Margolin A.A., Stolovitzky G., Klein U., la-Favera R., Califano A. Reverse engineering of regulatory networks in human B cells. Nat. Genet. 2005, 37:382-390.
9. Bhattacharjee A., Richards W.G., Staunton J., Li C., Monti S., Vasa P., Ladd C., Beheshti J., Bueno R., Gillette M., Loda M., Weber G., Mark E.J., Lander E.S., Wong W., Johnson B.E., Golub T.R., Sugarbaker D.J., Meyerson M. Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses. Proc. Natl Acad. Sci. USA 2001, 98:13790-13795.
10. Butte A.J., Kohane I.S. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac. Symp. Biocomput. 2000, 418-429.
11. Carrera J., Rodrigo G., Jaramillo A. Towards the automated engineering of a synthetic genome. Mol. Biosyst. 2009, 5:733-743.
12. Castelo R., Roverato A. A robust procedure for Gaussian graphical model search from microarray data with p larger than n. J. Mach. Learn. Res. 2006, 7:2650.
13. Castelo R., Roverato A. Reverse engineering molecular regulatory networks from microarray data with qp-graphs. J. Comput. Biol. 2009, 16:213-227.
14. Chickering D.M., Heckerman D., Meek C. Large-sample learning of Bayesian networks is NP-hard. J. Mach. Learn. Res. 2004, 5:1287-1330.
15. Chu T., Glymour C., Scheines R., Spirtes P. A statistical problem for inference to regulatory structure from associations of gene expression measurements with microarrays. Bioinformatics 2003, 19:1147-1152.
16. Davis J., Goadrich M. The relationship between precision-recall and roc curves. Proceedings of the 23rd international conference on Machine learning 2006, 240.
17. DeLong E.R., DeLong D.M., Clarke-Pearson D.L. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 1988, 44:837-845.
18. Duda R.O., Hart P.E., Stork D.G. Pattern Classification 2001, Wiley, New York.
19. Eisen M.B., Spellman P.T., Brown P.O., Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc. Natl Acad. Sci. USA 1998, 95:14863-14868.
20. Faith J.J., Driscoll M.E., Fusaro V.A., Cosgrove E.J., Hayete B., Juhn F.S., Schneider S.J., Gardner T.S. Many Microbe Microarrays Database: uniformly normalized Affymetrix compendia with structured experimental metadata. Nucleic Acids Res. 2008, 36:D866-D870.
21. Faith J.J., Hayete B., Thaden J.T., Mogno I., Wierzbowski J., Cottarel G., Kasif S., Collins J.J., Gardner T.S. Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol. 2007, 5:e8.
22. Frey L., Fisher D., Tsamardinos I., Aliferis C.F., Statnikov A. Identifying Markov blankets with decision tree induction. Proceedings of the Third IEEE International Conference on Data Mining (ICDM) 2003.
23. Friedman J., Hastie T., Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics 2008, 9:432-441.
24. Gama-Castro S., Jimenez-Jacinto V., Peralta-Gil M., Santos-Zavaleta A., Penaloza-Spinola M.I., Contreras-Moreira B., Segura-Salazar J., Muniz-Rascado L., Martinez-Flores I., Salgado H., Bonavides-Martinez C., Abreu-Goodger C., Rodriguez-Penagos C., Miranda-Rios J., Morett E., Merino E., Huerta A.M., Trevino-Quintanilla L., Collado-Vides J. RegulonDB (version 6.0): gene regulation model of Escherichia coli K-12 beyond transcription, active (experimental) annotated promoters and Textpresso navigation. Nucleic Acids Res. 2008, 36:D120-D124.
25. Guyon I., Aliferis C., Cooper G., Elisseeff A., Pellet J.P., Spirtes P., Statnikov A. Design and analysis of the causation and prediction challenge. J. Mach. Learn. Res. Workshop Conf. Proc. 2008, 3:1-33.
26. Hache H., Lehrach H., Herwig R. Reverse engineering of gene regulatory networks: a comparative study. EURASIP J. Bioinform. Syst. Biol. 2009, 617281.
27. Hand D.J., Till R.J. A simple generalisation of the area under the ROC curve for multiple class classification problems. Mach. Learn. 2001, 45:171-186.
28. Harbison C.T., Gordon D.B., Lee T.I., Rinaldi N.J., MacIsaac K.D., Danford T.W., Hannett N.M., Tagne J.B., Reynolds D.B., Yoo J., Jennings E.G., Zeitlinger J., Pokholok D.K., Kellis M., Rolfe P.A., Takusagawa K.T., Lander E.S., Gifford D.K., Fraenkel E., Young R.A. Transcriptional regulatory code of a eukaryotic genome. Nature 2004, 431:99-104.
29. Huttenhower C., Hibbs M.A., Myers C.L., Caudy A.A., Hess D.C., Troyanskaya O.G. The impact of incomplete knowledge on evaluation: an experimental benchmark for protein function prediction. Bioinformatics 2009, 25:2404-2410.
30. Jeong H., Tombor B., Albert R., Oltvai Z.N., Barabasi A.L. The large-scale organization of metabolic networks. Nature 2000, 407:651-654.
31. Lee T.I., Rinaldi N.J., Robert F., Odom D.T., Bar-Joseph Z., Gerber G.K., Hannett N.M., Harbison C.T., Thompson C.M., Simon I., Zeitlinger J., Jennings E.G., Murray H.L., Gordon D.B., Ren B., Wyrick J.J., Tagne J.B., Volkert T.L., Fraenkel E., Gifford D.K., Young R.A. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science 2002, 298:799-804.
32. Li X.Y., MacArthur S., Bourgon R., Nix D., Pollard D.A., Iyer V.N., Hechmer A., Simirenko L., Stapleton M., Luengo Hendriks C.L., Chu H.C., Ogawa N., Inwood W., Sementchenko V., Beaton A., Weiszmann R., Celniker S.E., Knowles D.W., Gingeras T., Speed T.P., Eisen M.B., Biggin M.D. Transcription factors bind thousands of active and inactive regions in the Drosophila blastoderm. PLoS Biol. 2008, 6:e27.
33. MacIsaac K.D., Wang T., Gordon D.B., Gifford D.K., Stormo G.D., Fraenkel E. An improved map of conserved regulatory sites for Saccharomyces cerevisiae. BMC Bioinform. 2006, 7:113.
34. Marbach D., Prill R.J., Schaffter T., Mattiussi C., Floreano D., Stolovitzky G. Revealing strengths and weaknesses of methods for gene network inference. Proc. Natl Acad. Sci. USA 2010, 107:6286-6291.
35. 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, 16:229-239.
36. Margolin A.A., Nemenman I., Basso K., Wiggins C., Stolovitzky G., Dalla F.R., Califano A. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinform. 2006, 7(Suppl 1):S7.
37. Meinshausen N., Buhlmann P. High-dimensional graphs and variable selection with the lasso. Ann. Stat. 2006, 34:1436-1462.
38. Opgen-Rhein R., Strimmer K. From correlation to causation networks: a simple approximate learning algorithm and its application to high-dimensional plant gene expression data. BMC Syst. Biol. 2007, 1:37.
39. Parkinson H., Kapushesky M., Kolesnikov N., Rustici G., Shojatalab M., Abeygunawardena N., Berube H., Dylag M., Emam I., Farne A., Holloway E., Lukk M., Malone J., Mani R., Pilicheva E., Rayner T.F., Rezwan F., Sharma A., Williams E., Bradley X.Z., Adamusiak T., Brandizi M., Burdett T., Coulson R., Krestyaninova M., Kurnosov P., Maguire E., Neogi S.G., Rocca-Serra P., Sansone S.A., Sklyar N., Zhao M., Sarkans U., Brazma A. ArrayExpress update-from an archive of functional genomics experiments to the atlas of gene expression. Nucleic Acids Res. 2009, 37:D868-D872.
40. Prill R.J., Marbach D., Saez-Rodriguez J., Sorger P.K., Alexopoulos L.G., Xue X., Clarke N.D., Altan-Bonnet G., Stolovitzky G. Towards a rigorous assessment of systems biology models: the DREAM3 challenges. PLoS ONE 2010, 5:e9202.
41. Qiu P., Gentles A.J., Plevritis S.K. Fast calculation of pairwise mutual information for gene regulatory network reconstruction. Comput. Meth. Programs Biomed. 2009, 94:177-180.
42. Rhodes D.R., Chinnaiyan A.M. Integrative analysis of the cancer transcriptome. Nat. Genet. 2005, 37(Suppl):S31-S37.
43. Richardson M., Domingos P. Markov logic networks. Mach. Learn. 2006, 62:107-136.
44. Statnikov A., Aliferis C.F. Analysis and computational dissection of molecular signature multiplicity. PLoS Computational Biol. 2010, 6:e1000790.
45. Stolovitzky G., Monroe D., Califano A. Dialogue on reverse-engineering assessment and methods: the DREAM of high-throughput pathway inference. Ann. N.Y. Acad. Sci. 2007, 1115:1-22.
46. Stolovitzky G., Prill R.J., Califano A. Lessons from the DREAM2 challenges. Ann. N.Y. Acad. Sci. 2009, 1158:159-195.
47. Vapnik V.N. Statistical Learning Theory 1998, Wiley, New York.
48. Watkinson J., Liang K.C., Wang X., Zheng T., Anastassiou D. Inference of regulatory gene interactions from expression data using three-way mutual information. Ann. N.Y. Acad. Sci. 2009, 1158:302-313.