Parallel information theory based construction of gene regulatory networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5374 LNCS, Issue , 2008, Pages 336-349

  Zola, Jaroslaw ^a   Aluru, Maneesha ^b   Aluru, Srinivas ^a  

^a Iowa State University   (United States)

^b IOWA STATE UNIVERSITY   (United States)

Author keywords

Gene networks; Mutual information; Parallel computational biology; Systems biology

Indexed keywords

BIOINFORMATICS; BIOLOGY; COMPUTATIONAL COMPLEXITY; DATA PROCESSING; FEATURE EXTRACTION; GENE EXPRESSION; GENES; HIGH PERFORMANCE LIQUID CHROMATOGRAPHY; INFORMATION THEORY; PARALLEL ALGORITHMS; PROGRAMMING THEORY;

ARABIDOPSIS (ARABIDOPSIS THALIANA); ARABIDOPSIS THALIANA (ELIP); BLUE GENE/L; GENE NETWORKS; GRAND CHALLENGE; MICROARRAY EXPERIMENTS; MUTUAL INFORMATION; NOVEL METHODS; PARALLEL COMPUTATIONAL BIOLOGY; PARALLELIZATION; PERMUTATION TESTING; SYSTEMS BIOLOGY;

COMPUTATIONAL METHODS;

EID: 58649124319     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-89894-8_31     Document Type: Conference Paper

References (27)

1. Zhu, X., Gerstein, M., Snyder, M.: Getting connected: analysis and principles of biological networks. Genes & development 21(9), 1010-1024 (2007)
2. The chipping forecast II. Special Supplement. Nature Genetics (2002)
3. Torres, T., Metta, M., Ottenwalder, B., et al.: Gene expression profiling by massively parallel sequencing. Genome research 18(1), 172-177 (2008)
4. Butte, A., Kohane, I.: Unsupervised knowledge discovery in medical databases using relevance networks. In: Proc. of American Medical Informatics Association Symposium, pp. 711-715 (1999)
5. D'haeseleer, P., Wen, X., Fuhrman, S., et al.: Mining the gene expression matrix: Inferring gene relationships from large scale gene expression data. In: Information Processing in Cells and Tissues (1998)
6. de la Fuente, A., Bing, N., Hoeschele, I., et al.: Discovery of meaningful associations in genomic data using partial correlation coefficients. Bioinformatics 20(18), 3565- 3574 (2004)
7. Schafer, J., Strimmer, K.: An empirical Bayes approach to inferring large-scale gene association networks. Bioinformatics 21(6), 754-764 (2005)
8. Friedman, N., Linial, M., Nachman, I., et al.: Using Bayesian networks to analyze expression data. Journal of Computational Biology 7, 601-620 (2000)
9. Yu, H., Smith, A., Wang, P., et al.: Using Bayesian network inference algorithms to recover molecular genetic regulatory networks. In: Proc. of International Conference on Systems Biology (2002)
10. Daub, C., Steuer, R., Selbig, J., et al.: Estimating mutual information using B- spline functions - an improved similarity measure for analysing gene expression data. BMC Bioinformatics 5, 118 (2004)
11. Hartemink, A.: Reverse engineering gene regulatory networks. Nature Biotechnology 23(5), 554-555 (2005)
12. Ma, S., Gong, Q., Bohnert, H.: An Arabidopsis gene network based on the graphical Gaussian model. Genome research 17(11), 1614-1625 (2007)
13. Basso, K., Margolin, A., Stolovitzky, G., et al.: Reverse engineering of regulatory networks in human B cells. Nature Genetics 37(4), 382-390 (2005)
14. Butte, A., Kohane, I.: Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. In: Pacific Symposium on Bio- computing, pp. 418-429 (2000)
15. Cover, T., Thomas, J.: Elements of Information Theory, 2nd edn. Wiley, Chichester (2006)
16. EMBL-EBI ArrayExpress (last visited) (2008), http://www.ebi.ac.uk/ microarray-as/aer/
17. NCBI Gene Expression Omnibus (last visited) (2008), http://www.ncbi.nlm. nih.gov/geo/
18. NASC European Arabidopsis Stock Centre (last visited) (2008), http://www.arabidopsis.info/
19. Schneidman, E., Still, S., Berry, M., et al.: Network information and connected correlations. Physical review letters 91 (23), 238701 (2003)
20. Khan, S., Bandyopadhyay, S., Ganguly, A., et al.: Relative performance of mutual information estimation methods for quantifying the dependence among short and noisy data. Physical review. E 76(2 Pt 2), 026209 (2007)
21. Moon, Y., Rajagopalan, B., Lall, U.: Estimation of mutual information using kernel density estimators. Physical review. E 52(3), 2318-2321 (1995)
22. Kraskov, A., Stogbauer, H., Grassberger, P.: Estimating mutual information. Physical review. E 69(6 Pt 2), 066138 (2004)
23. De Boor, C: A practical guide to splines. Springer, Heidelberg (1978)
24. Van den Bulcke, T., Van Leemput, K., Naudts, B., et al.: SynTReN: a generator of synthetic gene expression data for design and analysis of structure learning algorithms. BMC Bioinformatics 7, 43 (2006)
25. Palaniswamy, S., James, S., Sun, H., et al.: AGRIS and AtRegNet. A platform to link cis-regulatory elements and transcription factors into regulatory networks. Plant physiology 140(3), 818-829 (2006)
26. Statistical algorithms description document (last visited) (2008), http://www.affymetrix.com/
27. Irizarry, R., Warren, D., Spencer, F., et al.: Multiple-laboratory comparison of microarray platforms. Nature Methods 2, 345-350 (2005)