1. Opgen-Rhein R, Strimmer K (2007) Learning causal networks from systems
biology time course data: an effective model selection procedure for the vector
autoregressive process. BMC Bioinformatics 8: S3.
2. Bernard A, Hartemink AJ (2005) Informative structure priors: joint learning of
dynamic regulatory networks from multiple types of data. Proceedings of the
Pacific Symposium on Biocomputing. pp 459–70.
3. Lebre S (2009) Inferring dynamic genetic networks with low order independencies.
Statistical Applications in Genetics and Molecular Biology 8.
4. Granger CWJ (1969) Investigating causal relations by econometric models and
cross-spectral methods. Econometrica 37: 424–438.
5. Zou C, Feng J (2009) Granger causality vs. dynamic bayesian network inference:
a comparative study. BMC Bioinformatics 10: 122+.
6. Marinazzo D, Pellicoro M, Stramaglia S (2008) Kernel-granger causality and the
analysis of dynamical networks. Physical Review E (Statistical, Nonlinear, and
Soft Matter Physics) 77.
7. Scha¨ fer J, Strimmer K (2005) An empirical Bayes approach to inferring largescale
gene association networks. Bioinformatics 21: 754–64.
8. Veiga D, Vicente F, Grivet M, de la Fuente A, Vasconcelos A (2007) Genomewide
partial correlation analysis of escherichia coli microarray data. Genet Mol
Res 6: 730–742.
9. 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.
10. Stolovitzky G, Monroe DON, Califano A (2007) Dialogue on reverseengineering
assessment and methods: The dream of high-throughput pathway
inference. Annals of the New York Academy of Sciences 1115: 1–22.
11. Ltkepohl H (2006) New Introduction to Multiple Time Series Analysis. Springer
Publishing Company, Incorporated.
12. Mukhopadhyay NDD, Chatterjee S (2006) Causality and pathway search in
microarray time series experiment. Bioinformatics.
13. Benjamini Y, Hochberg Y (1995) Controlling the false discovery rate: A practical
and powerful approach to multiple testing. Journal of the Royal Statistical
Society B: 289–300.
14. Bro R, Sidiropoulos ND, Smilde AK (2002) Maximum likelihood fitting using
ordinary least squares algorithms. Journal of Chemometrics 16: 387–400.
15. Egan J (1975) Signal detection theory and ROC analysis, Series in Cognition
and Perception. New York, NY, USA: Academic Press.
16. Fawcett T (2006) An introduction to roc analysis. Pattern Recogn Lett 27:
861–874.
17. Rijsbergen CJV (1979) Information Retrieval. Newton, MA, USA: Butterworth-
Heinemann.
18. Smith S, Fulton D, Chia T, Thorneycroft D, Chapple A, et al. (2004) Diurnal
changes in the transcriptom encoding enzymes of starch metabolism provide
evidence for both transcriptionaland posttranscriptional regulation of starch
metabolism inarabidopsis leaves. Plant Physiology 136: 2687–2699.
19. Zeeman SC, Smith SM, Smith AM (2007) The diurnal metabolism of leaf
starch. Biochem J 401: 13–28.
20. Wichert S, Fokianos K, Strimmer K (2004) Identifying periodically expressed
transcripts in microarray time series data. Bioinformatics 20: 5–20.
21. Lee H, Kong SW, Park PJ (2008) Integrative analysis reveals the direct and
indirect interactions between dna copy number aberrations and gene expression
changes. Bioinformatics 24: 889–896.
22. Tanay ASR, Sharan R (2002) Discovering statistically significant biclusters in
gene expression data. Bioinformatics 18(1): S136–144.
23. The Gene Ontology Consortium (2000) Gene ontology: tool for the unification
of biology. Nature Genetics 25: 25–29.
24. Wilhelm KS, Thomashow MF (1993) Arabidopsis thaliana cor15b, an apparent
homologue of cor15a, is strongly responsive to cold and aba, but not drought.
Plant Mol Biol 23: 1073–7.
25. Lin C, Thomashow MF (1992) Dna sequence analysis of a complementary dna
for cold-regulated arabidopsis gene cor15 and characterization of the cor 15
polypeptide. Plant Physiol 99: 519–525.
26. Saibo NJM, Lourenco T, Oliveira MM (2009) Transcription factors and
regulation of photosynthetic and related metabolism under environmental
stresses. Ann Bot 103: 609–623.
27. Rook F, Hadingham SA, Li Y, Bevan MW (2006) Sugar and aba response
pathways and the control of gene expression. Plant Cell Environ 29: 426–34.
28. Welch BL (1947) The generalization of ‘‘student’s’’ problem when several
different population variances are involved. Biometrika 34: 28–35.
29. Schaffer R (1998) The late elongated hypocotyl mutation of arabidopsis disrupts
circadian rhythms and the photoperiodic control of flowering. Cell 93:
1219–1229.
30. Wang ZY, Kenigsbuch D, Sun L, Harel E, Ong M, et al. (1997) A myb-related
transcription factor is involved in the phytochrome regulation of an arabidopsis
lhcb gene. PlantCell 9: 491–507.