NetDiff – Bayesian model selection for differential gene regulatory network inference

Scientific Reports

Volumn 6, Issue 1, 2016, Pages

  Thorne, Thomas ^a  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEMENT COMPONENT C9;

ALGORITHM; AMYOTROPHIC LATERAL SCLEROSIS; AREA UNDER THE CURVE; BAYES THEOREM; DNA MICROARRAY; GENE REGULATORY NETWORK; GENETIC POLYMORPHISM; GENETICS; HUMAN; NORMAL DISTRIBUTION; PATHOLOGY; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC;

ALGORITHMS; AMYOTROPHIC LATERAL SCLEROSIS; AREA UNDER CURVE; BAYES THEOREM; COMPLEMENT C9; GENE REGULATORY NETWORKS; HUMANS; NORMAL DISTRIBUTION; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; POLYMORPHISM, GENETIC; ROC CURVE;

EID: 85046412751     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep39224     Document Type: Article

References (28)

1. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 1 (2008).
2. 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 Systems Biology 1, 1, 37 (2007).
3. Wang, T. et al. FastGGM: An Efficient Algorithm for the Inference of Gaussian Graphical Model in Biological Networks. PLOS Computational Biology 12, e1004755 (2016).
4. Margolin, A. A. et al. ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context. BMC Bioinformatics 7 Suppl 1, S7 (2006).
5. Bandyopadhyay, S. et al. Rewiring of Genetic Networks in Response to DNA Damage. Science 330, 1385–1389 (2010).
6. West, J., Bianconi, G., Severini, S. & Teschendorff, A. E. Differential network entropy reveals cancer system hallmarks. Scientific Reports 2, 802 (2012).
7. Ideker, T. & Krogan, N. J. Differential network biology. Molecular Systems Biology 8 (2012).
8. Thorne, T. et al. Graphical modelling of molecular networks underlying sporadic inclusion body myositis. Mol. BioSyst. 9, 1736–1742 (2013).
9. Okawa, S., Angarica, V. E., Lemischka, I., Moore, K. & del Sol, A. A differential network analysis approach for lineage specifier prediction in stem cell subpopulations. npj Systems Biology and Applications 1, 15012 (2015).
10. Krämer, N., Schäfer, J. & Boulesteix, A. L. Regularized estimation of large-scale gene association networks using graphical Gaussian models. BMC Bioinformatics 10, 384 (2009).
11. Caron, F. & Doucet, A. Sparse Bayesian nonparametric regression. In ICML ’08, 88–95 (ACM, New York, New York, USA, 2008).
12. Strimmer, K. fdrtool: a versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics 24, 1461–1462 (2008).
13. MacKay, D. J. C. Developments in Probabilistic Modelling with Neural Networks — Ensemble Learning. In Machine Learning, 191–198 (Springer London, London, 1995).
14. MacKay, D. J. C. Information Theory, Inference and Learning Algorithms (Cambridge University Press, 2003).
15. Bishop, C. M. Pattern Recognition and Machine Learning (Springer Verlag, 2006).
16. Barber, D. Bayesian Reasoning and Machine Learning (Cambridge University Press, 2012).
17. Murphy, K. P. Machine Learning. A Probabilistic Perspective (MIT Press, 2012).
18. Green, P. J. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82, 711–732 (1995).
19. R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org/ (2016).
20. Grau, J., Grosse, I. & Keilwagen, J. PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R. Bioinformatics 31, 2595–2597 (2015).
21. Cooper-Knock, J. et al. C9ORF72 GGGGCC Expanded Repeats Produce Splicing Dysregulation which Correlates with Disease Severity in Amyotrophic Lateral Sclerosis. PLOS One 10, e0127376 (2015).
22. DeJesus-Hernandez, M. et al. Expanded GGGGCC Hexanucleotide Repeat in Noncoding Region of C9ORF72 Causes Chromosome 9p-Linked FTD and ALS. Neuron 72, 245–256 (2011).
23. Kanehisa, M., Goto, S., Furumichi, M., Tanabe, M. & Hirakawa, M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Research 38, D355–60 (2010).
24. Kanehisa, M., Goto, S., Kawashima, S. & Nakaya, A. The KEGG databases at GenomeNet. Nucleic Acids Research 30, 42–46 (2002).
25. Vukosavic, S., Dubois-Dauphin, M., Romero, N. & Przedborski, S. Bax and Bcl-2 interaction in a transgenic mouse model of familial amyotrophic lateral sclerosis. Journal of neurochemistry 73, 2460–2468 (1999).
26. Dâ Ambrosi, N., Rossi, S., Gerbino, V. & Cozzolino, M. Rac1 at the crossroad of actin dynamics and neuroinflammation in Amyotrophic Lateral Sclerosis. Frontiers in Cellular Neuroscience 8, 1328 (2014).
27. Liu, Z. et al. NR2B-Containing NMDA Receptors Expression and Their Relationship to Apoptosis in Hippocampus of Alzheimer’s Disease-Like Rats. Neurochemical Research 37, 1420–1427 (2012).
28. Sathasivam, S. & Shaw, P. J. Apoptosis in amyotrophic lateral sclerosis–what is the evidence? The Lancet Neurology 4, 500–509 (2005).