Abstract
Elucidating the structure of gene regulatory networks (GRN), i.e., identifying which genes are under control of which transcription factors, is an important challenge to gain insight on a cell’s working mechanisms. We present SIRENE, a method to estimate a GRN from a collection of expression data. Contrary to most existing methods for GRN inference, SIRENE requires as input a list of known regulations, in addition to expression data, and implements a supervised machine-learning approach based on learning from positive and unlabeled examples to account for the lack of negative examples.
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References
Hood L, Heath JR, Phelps ME, Lin B (2004) Systems biology and new technologies enable predictive and preventative medicine. Science 306(5696):640–643
Bansal M, Belcastro V, Ambesi-Impiombato A, diBernardo D (2007) How to infer gene networks from expression profiles. Mol Syst Biol 3:78
Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM (1999) Systematic determination of genetic network architecture. Nat Genet 22:281–285
Butte AJ, Tamayo P, Slonim D, Golub TR, Kohane IS (2000) Discovering functional relationships between RNA expression and chemotherapeutic susceptibility using relevance networks. Proc Natl Acad Sci USA 97(22):12182–12186
Margolin AA, Nemenman I, Basso K, Wiggins C, Stolovitzky G, Dalla Favera R, Califano A (2006) ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular contexts. BMC Bioinformatics 7 Suppl 1:S7
Faith JJ, Hayete B, Thaden JT, Mogno I, Wierzbowski J, Cottarel G, Kasif S, Collins JJ, Gardner TS (2007) Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles. PLoS Biol 5(1):e8
Friedman N, Linial M, Nachman I, Pe’er D (2000) Using Bayesian networks to analyze expression data. J Comput Biol 7(3–4):601–620
Chen T, He HL, Church GM (1999) Modeling gene expression with differential equations. Pac Symp Biocomput 4:29–40
Tegner J, Yeung MKS, Hasty J, Collins JJ (2003) Reverse engineering gene networks: integrating genetic perturbations with dynamical modeling. Proc Natl Acad Sci USA 100(10):5944–5949
Gardner TS, Bernardo D, Lorenz D, Collins JJ (2003) Inferring genetic networks and identifying compound mode of action via expression profiling. Science 301(5629):102–105
Chen K-C, Wang T-Y, Tseng H-H, Huang C-YF, Kao C-Y (2005) A stochastic differential equation model for quantifying transcriptional regulatory network in Saccharomyces cerevisiae. Bioinformatics 21(12):2883–2890
Bernardo D, Thompson MJ, Gardner TS, Chobot SE, Eastwood EL, Wojtovich AP, Elliott SJ, Schaus SE, Collins JJ (2005) Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nat Biotechnol 23(3):377–383
Bansal M, Della Gatta G, Bernardo D (2006) Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics 22(7):815–822
Akutsu T, Miyano S, Kuhara S (2000) Algorithms for identifying Boolean networks and related biological networks based on matrix multiplication and fingerprint function. J Comput Biol 7(3–4):331–343
Yamanishi Y, Vert J-P, Kanehisa M (2004) Protein network inference from multiple genomic data: a supervised approach. Bioinformatics 20:i363–i370
Vert J-P, Yamanishi Y (2005) Supervised graph inference. In: Saul LK, Weiss Y, Bottou L (eds) Advances in neural information processing systems, vol 17. MIT, Cambridge, MA, pp 1433–1440
Yamanishi Y, Vert J-P, Kanehisa M (2005) Supervised enzyme network inference from the integration of genomic data and chemical information. Bioinformatics 21:i468–i477
Bleakley K, Biau G, Vert J-P (2007) Supervised reconstruction of biological networks with local models. Bioinformatics 23(13):i57–i65
Mordelet F, Vert J-P (2008) SIRENE: Supervised inference of regulatory networks. Bioinformatics 24(16):i76–i82
Bishop C (2006) Pattern recognition and machine learning. Springer, Berlin
Schölkopf B, Platt JC, Shawe-Taylor J, Smola AJ, Williamson RC (2001) Estimating the Support of a High-Dimensional Distribution. Neural Comput 13:1443–1471
Denis F, Gilleron R, Letouzey F (2005) Learning from positive and unlabeled examples. Theoret Computer Sci 348(1):70–83
Scott C, Blanchard G (2009) Novelty detection: unlabeled data definitely help. In: van Dyk V, Welling M (ed) Proceedings of the twelfth international conference on artificial intelligence and statistics (AISTATS), vol 5. Clearwater Beach, Florida, pp 464–471
Manevitz LM, Yousef M (2001) One-class SVMs for document classification. J Mach Learn Res 2:139–154
Liu B, Lee WS, Yu PS, Li X (2002) Partially supervised classification of text documents. In: ICML ’02: Proceedings of the Nineteenth International Conference on Machine Learning, San Francisco, CA, USA. Morgan Kaufmann Publishers, USA, pp 387–394
Li X, Liu B (2003) Learning to classify texts using positive and unlabeled data. In: IJCAI’03: Proceedings of the 18th international joint conference on Artificial intelligence San Francisco, CA. Morgan Kaufmann Publishers, USA, pp 587–592
Liu B, Dai Y, Li X, Lee WS, Yu PS (2003) Building text classifiers using positive and unlabeled examples. In: International Conference on Data Mining, pp 179–186
Yu H, Han J, Chang KC-C (2004) PEBL: Web page classification without negative examples. IEEE Trans Knowl Data Eng 16(1):70–81
Lee WS, Liu B (2003) Learning with positive and unlabeled examples using weighted logistic regression. In: Fawcett T, Mishra N (ed) Machine learning, proceedings of the twentieth international conference (ICML 2003). AAAI Press, USA, pp 448–455
Elkan C, Noto K (2008) Learning classifiers from only positive and unlabeled data. In: KDD ’08: Proceeding of the 14th ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, USA, pp 213–220
Mordelet F, Vert J-P (2010) A bagging SVM to learn from positive and unlabeled examples. Technical Report HAL:00523336
Salgado H, Gama-Castro S, Peralta-Gil M, DÃaz-Peredo E, Sánchez-Solano F, Santos-Zavaleta A, MartÃnez-Flores I, Jiménez-Jacinto V, Bonavides-MartÃnez C, Segura-Salazar J, MartÃnez-Antonio A, Collado-Vides J (2006) RegulonDB (version 5.0): Escherichia coli K-12 transcriptional regulatory network, operon organization, and growth conditions. Nucleic Acids Res 34(Database issue):D394–D397
Breiman L (1996) Bagging predictors. Mach Learn 24(2):123–140
Vapnik VN (1998) Statistical learning theory. Wiley, New York
Schölkopf B, Tsuda K, Vert J-P (2004) Kernel methods in computational biology. MIT, Cambridge, MA
Vert R, Vert J-P (2006) Consistency and convergence rates of one-class SVMs and related algorithms. J Mach Learn Res 7:817–854
Mordelet F (2010) Learning from positive and unlabeled examples in biology. Ph.D. thesis, Mines ParisTech
Joachims T (1997) A probabilistic analysis of the Rocchio algorithm with TFIDF for text categorization. In: ICML ’97: Proceedings of the fourteenth international conference on machine learning, Nashville, Tennessee. Morgan Kaufmann Publishers, USA, pp 143–151
De Bie T, Tranchevent L-C, vanOeffelen LMM, Moreau Y (2007) Kernel-based data fusion for gene prioritization. Bioinformatics 23(13):i125–i132
Zhang K, Tsang I, Kwok J (2009) Maximum margin clustering made practical. IEEE Trans Neural Network 20(4):583–596
Chang C-C, Lin C-J (2001) LIBSVM: a library for support vector machines. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
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Mordelet, F., Vert, JP. (2013). Supervised Inference of Gene Regulatory Networks from Positive and Unlabeled Examples. In: Mamitsuka, H., DeLisi, C., Kanehisa, M. (eds) Data Mining for Systems Biology. Methods in Molecular Biology, vol 939. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-107-3_5
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DOI: https://doi.org/10.1007/978-1-62703-107-3_5
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