NIMEFI: Gene regulatory network inference using multiple ensemble feature importance algorithms

PLoS ONE

Volumn 9, Issue 3, 2014, Pages

  Ruyssinck, Joeri ^a   Huynh Thu, Vân Anh ^b,c   Geurts, Pierre ^b   Dhaene, Tom ^a   Demeester, Piet ^a   Saeys, Yvan ^a,d  

^a GHENT UNIVERSITY   (Belgium)

^b UNIVERSITY OF LIÈGE   (Belgium)

^c UNIVERSITY OF EDINBURGH   (United Kingdom)

^d DEPARTMENT OF PLANT SYSTEMS BIOLOGY   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS OF VARIANCE; AREA UNDER THE CURVE; ARTICLE; CONTROLLED STUDY; CORRELATION COEFFICIENT; DREAM4 ALGORITHM; DREAM5 ALGORITHM; GENE CONTROL; GENE REGULATORY NETWORK; GENENETWEAVER ALGORITHM; GENETIC ALGORITHM; GENIE3 ALGORITHM; HIGH THROUGHPUT SCREENING; NETWORK INFERENCE USING MULTIPLE ENSEMBLE FEATURE IMPORTANCE ALGORITHM; RECEIVER OPERATING CHARACTERISTIC; SUPPORT VECTOR MACHINE; SYNTREN 100 ALGORITHM; ALGORITHM; BIOLOGICAL MODEL; GENE EXPRESSION REGULATION; PHYSIOLOGY;

ALGORITHMS; GENE EXPRESSION REGULATION; GENE REGULATORY NETWORKS; MODELS, GENETIC;

EID: 84899876508     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0092709     Document Type: Article

References (36)

1. Madhamshettiwar PB, Maetschke SR, Davis MJ, Reverter A, Ragan MA (2012) Gene regulatory network inference: evaluation and application to ovarian cancer allows the prioritization of drug targets. Genome Medicine 4: 41.
2. Michoel T, De Smet R, Joshi A, Van de Peer Y, Marchal K (2009) Comparative analysis of module-based versus direct methods for reverse-engineering transcriptional regulatory networks. BMC Systems Biology 3: 49.
3. De Smet R, Marchal K (2010) Advantages and limitations of current network inference methods. Nat Rev Micro 8: 717-729.
4. Eisen MB, Spellman PT, Brown PO, Botstein D (1998) Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences 95: 14863-14868. (Pubitemid 29003722)
5. Butte AJ, Kohane IS (2000) Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. In: Pacific Symposium on Biocomputing. 418-429.
6. Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, et al. (2007) Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles. PLoS Biol 5: e8.
7. Margolin AA, Wang K, Lim WK, Kustagi M, Nemenman I, et al. (2006) Reverse engineering cellular networks. Nature Protocols 1: 662-671.
8. Meyer P, Kontos K, Lafitte F, Bontempi G (2007) Information-theoretic inference of large transcriptional regulatory networks. EURASIP journal on bioinformatics & systems biology.
9. Ding C, Peng H (2003) Minimum Redundancy Feature Selection from Microarray Gene Expression Data. In: J Bioinform Comput Biol. 523-529.
10. Altay G, Streib F (2010) Inferring the conservative causal core of gene regulatory networks. BMC Systems Biology 4: 132.
11. de Matos Simoes R, Emmert-Streib F (2012) Bagging statistical network inference from large-scale gene expression data. PLoS ONE 7: e33624.
12. Küffner R, Petri T, Tavakkolkhah P, Windhager L, Zimmer R (2012) Inferring gene regulatory networks by ANOVA. Bioinformatics 28: 1376-1382.
13. Haury AC, Mordelet F, Vera-Licona P, Vert JP (2012) TIGRESS: Trustful Inference of Gene REgulation using Stability Selection. BMC Systems Biology 6: 145.
14. Soranzo N, Bianconi G, Altafini C (2007) Comparing association network algorithms for reverse engineering of large-scale gene regulatory networks: synthetic versus real data. Bioinformatics 23: 1640-1647. (Pubitemid 47244454)
15. Hache H, Lehrach H, Herwig R (2009) Reverse engineering of gene regulatory networks: a comparative study. EURASIP J Bioinformatics Syst Biol 2009: 8: 1-8: 12.
16. Narendra V, Lytkin NI, Aliferis CF, Statnikov A (2011) A comprehensive assessment of methods for de-novo reverse-engineering of genome-scale regulatory networks. Genomics 97: 7-18.
17. Marbach D, Costello J, Kuffner R, Vega N, Prill R, et al. (2012) Wisdom of crowds for robust gene network inference. Nat Meth 9: 796-804.
18. Marbach D, Schaffter T, Mattiussi C, Floreano D (2009) Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods. Journal of computational biology 16: 229-239.
19. Marbach D, Prill RJ, Schaffter T, Mattiussi C, Floreano D, et al. (2010) Revealing strengths and weaknesses of methods for gene network inference. Proceedings of the National Academy of Sciences 107: 6286-6291.
20. Prill RJ, Marbach D, Saez-Rodriguez J, Sorger PK, Alexopoulos LG, et al. (2010) Towards a Rigorous Assessment of Systems Biology Models: The DREAM3 Challenges. PLoS ONE 5: e9202.
21. Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P (2010) Inferring Regulatory Networks from Expression Data Using Tree-Based Methods. PLoS ONE 5.
22. Van den Bulcke T, Van Leemput K, Naudts B, van Remortel P, Ma H, et al. (2006) SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. BMC Bioinformatics 7: 43.
23. Schaffter T, Marbach D, Floreano D (2011) GeneNetWeaver: In silico benchmark generation and performance profiling of network inference methods. Bioinformatics 27: 2263-2270.
24. Guyon I, Elisseeff A (2003) An introduction to variable and feature selection. J Mach Learn Res 3: 1157-1182.
25. Saeys Y, Inza IN, Larrañaga P (2007) A review of feature selection techniques in bioinformatics. Bioinformatics 23: 2507-2517. (Pubitemid 350048351)
26. Abeel T, Helleputte T, Van de Peer Y, Dupont P, Saeys Y (2010) Robust biomarker identification for cancer diagnosis with ensemble feature selection methods. Bioinformatics 26: 392-398.
27. Bach FR (2008) Bolasso: model consistent Lasso estimation through the bootstrap. In: Proceedings of the 25th international conference on Machine learning. New York, NY, USA: ACM, ICML '08, pp. 33-40.
28. Saeys Y, Abeel T, Van de Peer Y (2008) Robust Feature Selection Using Ensemble Feature Selection Techniques. In: Machine Learning and Knowledge Discovery in Databases SE - Lecture Notes in Computer Science, Berlin, Heidelberg: Springer Berlin/Heidelberg, volume 5212. pp. 313-325.
29. Breiman L (2001) Random Forests. In: Machine Learning. pp. 5-32.
30. Guyon I, Weston J, Barnhill S, Vapnik V (2002) Gene Selection for Cancer Classification using Support Vector Machines. Machine Learning 46: 389-422. (Pubitemid 34129977)
31. Chang CC, Lin CJ (2011) LIBSVM: A library for support vector machines. ACM Trans Intell Syst Technol 2: 27: 1-27: 27.
32. Zou H, Hastie T (2005) Regularization and variable selection via the Elastic Net. Journal of the Royal Statistical Society, Series B 67: 301-320. (Pubitemid 40465877)
33. Friedman J, Hastie T, Tibshirani R (2009) Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33: 1-22.
34. Smits G, Kotanchek M (2005) Pareto-Front Exploitation in Symbolic Regression. In: OReilly UM, Yu T, Riolo R, Worzel B, editors, Genetic Programming Theory and Practice II SE - 17, Springer US, volume 8 of Genetic Programming. pp. 283-299.
35. Vladislavleva K, Veeramachaneni K, Burland M, Parcon J, O'Reilly UM (2010) Knowledge mining with genetic programming methods for variable selection in avor design. In: Proceedings of the 12th annual conference on Genetic and evolutionary computation. New York, NY, USA: ACM, GECCO '10, pp. 941-948.
36. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and Regression Trees. Belmont, CA: Wadsworth International Group.