Nested effects models for learning signaling networks from perturbation data

Biometrical Journal

Volumn 51, Issue 2, 2009, Pages 304-323

  Fröhlich, Holger ^a   Tresch, Achim ^b   Beißbarth, Tim ^a  

^a GERMAN CANCER RESEARCH CENTER DKFZ   (Germany)

^b UNIVERSITY OF MUNICH   (Germany)

Author keywords

Nested effects models; Perturbation data; Signaling pathway inference

Indexed keywords

BIOCHIPS; CANCER CELLS; CELL SIGNALING; SIGNALING;

COMPLEX CELLULAR PROCESS; EFFECT MODEL; GAIN INSIGHT; MEASUREMENTS OF; NESTED EFFECT; NESTED EFFECT MODEL; PERTURBATION DATA; SIGNALING NETWORKS; SIGNALING PATHWAY INFERENCE; SIGNALLING PATHWAYS;

GENES;

ESTROGEN RECEPTOR ALPHA;

COMPUTER PROGRAM; FEMALE; HUMAN; METABOLISM; REVIEW; SIGNAL TRANSDUCTION; STATISTICAL MODEL; TUMOR CELL LINE;

CELL LINE, TUMOR; ESTROGEN RECEPTOR ALPHA; FEMALE; HUMANS; MODELS, STATISTICAL; SIGNAL TRANSDUCTION; SOFTWARE;

EID: 66349107768     PISSN: 03233847     EISSN: 15214036     Source Type: Journal    
DOI: 10.1002/bimj.200800185     Document Type: Article

References (29)

1. Aho, A., Garey, M., Ullman, J. (1972). The transitive reduction of a directed graph. SIAM Journal on Computing 1, 131-137.
2. Benjamini, Y., Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B 57, 289-300.
3. Boutros, M., Agaisse, H., Perrimon, N. (2002). Sequential activation of signaling pathways during innate immune responses in Drosophila. Developmental Cell 3, 711-722.
4. Dempster, A., Laird, N., Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B 39, 1-38.
5. Driessche, N. V., Demsar, J., Booth, E., Hill, P., Juvan, P., Zupan, B., Kuspa, A., Shaulsky, G. (2005). Epistasis analysis with global transcriptional phenotypes. Nature Genetics 37, 471-477.
6. Fire, A., Xu, S., Montgomery, M., Kostas, S., Driver, S., Mello, C. (1998). Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 391, 806-811.
7. Fröhlich, H., Fellmann, M., Sültmann, H., Poustka, A., Beiβbarth, T. (2007). Large scale statistical inference of signaling pathways from RNAi and microarray data. BMC Bioinformatics 8, 386.
8. Fröhlich, H., Fellmann, M., Sültmann, H., Poustka, A., Beiβbarth, T. (2008). Estimating large scale signalling networks through nested effect models with intervention effects from microarray data. Bioinformatics 1, doi: 10.1093/bioinformatics/btm634.
9. Gat-Viks, I., Tanay, A., Raijman, D., Shamir, R. (2006). A probabilistic methodology for integrating knowledge and experiments. Journal of Computational Biology 13, 165-181.
10. Hastie, T., Tibshirani, R., Friedman, J. (2001). The Elements of Statistical Learning. Springer, NY, USA.
11. Huber, W., Heydebreck, A., Sültmann, H., Poustka, A., Vingron, M. (2002). Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics 18, S96-S104.
12. Kanehisa, M., Araki, M., Goto, S., Hattori, M., Hirakawa, M., Itoh, M., Katayama, T., Kawashima, S., Okuda, S., Tokimatsu, T., Yamanishi, Y. (2008). KEGG for linking genomes to life and the environment. Nucleic Acids Research 36, D480-D484.
13. Ljung, L. (1999). System Identification - Theory for the User. Prentice Hall, Englewood Cliffs, NJ.
14. Markowetz, F., Bloch, J., Spang, R. (2005). Non-transcriptional pathway features reconstructed from secondary effects of RNA interference. Bioinformatics 21, 4026-4032.
15. Markowetz, F., Kostka, D., Troyanskaya, O., Spang, R. (2007). Nested effects models for high-dimensional phenotyping screens. Bioinformatics 23, i305-i312.
16. Pearl, J. (2000). Causality: Models, Reasoning and Inference. Cambridge University Press, Cambridge.
17. Pe'er, D., Regev, A., Elidan, G., Friedman, N. (2001). Inferring subnetworks from perturbed expression profiles. Bioinformatics 17, S215-S224.
18. Pounds, S., Morris, S. (2003). Estimating the occurence of false positives and false negatives in microarray studies by approximating and partitioning the empirical distribution of p-values. Bioinformatics 19, 1236-1242.
19. Rogers, S., Girolami, M. (2005). A Bayesian regression approach to the inference of regulatory networks from gene expression data. Bioinformatics 21, 3131-3137.
20. Rung, J., Schlitt, T., Brazma, A., Freivalds, K., Vilo, J. (2002). Building and analysing genome-wide gene disruption networks. Bioinformatics 18, S202-S210.
21. Russel, S., Norvig, P. (1995). Artificial Intelligence - A Modern Approach. Prentice Hall, Englewood Cliffs, NJ.
22. Sachs, K., Perez, O., Pe'er, D., Lauffenburger, D., Nolan, G. (2005). Causal protein-signaling networks derived from multiparameter single-cell data. Science 208, 523-529.
23. Sedgewick, R. (2002). Algorithms. Addison-Wesley, Reading, MA.
24. Smyth, G., (2004). Linear models and empirical Bayes methods for assessing differential expression in microarray experiments. Statistical Applications in Genetics and Molecular Biology 3.
25. Tikhonov, A., Arsenin, V. (1977). Solutions of Ill-Posed Problems. W.H. Winston.
26. Tresch, A., Beiβbarth, T., Sültmann, H., Kuner, R., Poustka, A., Buness, A. (2007). Discrimination of direct and indirect interactions in a network of regulatory effects. Journal of Computional Biology 14, 1217-1228.
27. Tresch, A., Markowetz, F. (2008). Structure learning in nested effects models. Statistical Applications in Genetics and Molecular Biology 7.
28. 2 easy steps. Bioinformatics 17, 1183-1197.
29. Werhli, A., Husmeier, D. (2007). Reconstructing gene regulatory networks with bayesian networks by combining expression data with multiple sources of prior knowledge. Statistical Applications in Genetics and Molecular Biology 6.