유전자 조절 네트워크 추론에 관한 현안과제 및 방법론

  • 발행 : 2014.10.16

초록

키워드

참고문헌

  1. Zhao, Y, et al., "Differentially expressed gene profiles between multidrug resistant gastric adenocarcinoma cells and their parental cells", Cancer letters, Vol. 185, No. 2, pp 211-218, 2002. https://doi.org/10.1016/S0304-3835(02)00264-1
  2. Xu, J., et al., "Identification of differentially expressed genes in human prostate cancer using subtraction and microarray", Cancer research, Vol. 60, No. 6, pp 1677-1682, 2000.
  3. Hough, C., et al., "Large-scale serial analysis of gene expression reveals genes differentially expressed in ovarian cance", Cancer Research, Vol. 60, No. 22, pp 6281-6287, 2000.
  4. Gatta, D., et al., "Reverse engineering of TLX oncogenic transcriptional networks identifies RUNX1 as tumor suppressor in T-ALL", Nature medicine, Vol. 18, No. 3, pp 436-440, 2012. https://doi.org/10.1038/nm.2610
  5. Ravasi, T., et al., "An atlas of combinatorial transcriptional regulation in mouse and man", Cell, Vol. 140, No. 5, pp 744-752, 2010. https://doi.org/10.1016/j.cell.2010.01.044
  6. Sanders, D.A., et al., "Genome-wide mapping of FPXM1 binding reveals co-binding with estrogen receptor alpha in breast cancer cells", Genome biology, Vol. 14, No. 1, pp R6, 2013 https://doi.org/10.1186/gb-2013-14-1-r6
  7. Margolin, AA, et al., "ARACNE: an algorithm for the reconstruction of gene regulatory networks in a mammalian cellular context", BMC bioinformatics, Vol. 7, No. Suppl 1, pp S7, 2006.
  8. Fletcher, M, et al., "Master regulators of FGFR2 signalling and breast cancer risk", Nature communications, Vol. 4, No. 2464, 2013.
  9. Stolovitzky, G., et al., "Dialogue on Reverse-Engineering Assessment and Methods", Annals of the New York Academy of Sciences, Vol. 1115, No. 1, pp 1-22, 2007. https://doi.org/10.1196/annals.1407.021
  10. Marbach, D., et al., "Wisdom of crowds for robust gene network inference", Nature methods, Vol. 9, No. 8, pp 796-804, 2012. https://doi.org/10.1038/nmeth.2016
  11. Soneson, C. and Delorenzi, M., "A comparison of methods for differential expression analysis of RNA-seq data", BMC bioinformatics, Vol. 14, No. 1, pp 91, 2013. https://doi.org/10.1186/1471-2105-14-91
  12. Zwiener, I., et al., "Transformina RNA-Seq data to improve the perfOlmance of prognostic gene slgnatures", PloS one, Vol. 9, No. 1, pp e85150, 2014 https://doi.org/10.1371/journal.pone.0085150
  13. Zhang, L. and Mallick, B.K., "Inferring gene networks from discrete expression data", Biostatistics, Vol. 14, No. 4, pp 708-722, 2013 https://doi.org/10.1093/biostatistics/kxt021
  14. Gallopin, M., et al., "A Hierarchical Poisson Log-Normal Model for Network Inference from RNA Sequencing Data", PloS one, Vol. 8, No. 10, pp e77503, 2013. https://doi.org/10.1371/journal.pone.0077503
  15. Siletz, A., et al., "Dynamic transcription factor networks in epithelial-mesenchymal transition in breast cancer models", PloS one, Vol. 8, No. 4, pp e57180, 2013. https://doi.org/10.1371/journal.pone.0057180
  16. Wan, J., et al., "Integrative analysis of tissue-specific methylation and alternative splicing identifies conserved transcription factor binding motifs", Nucleic acids research, Vol. 41, No. 18, pp 8503-8514, 2013. https://doi.org/10.1093/nar/gkt652
  17. Zhang, X., et al., "NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference", Bioinformatics, Vol. 29, No. 1, pp 106-113, 2013. https://doi.org/10.1093/bioinformatics/bts619
  18. Haury, A.C., et al.,",TIGRESS: trustful inference of gene regulation using stability selection", BMC systems biology, Vol. 6, No. 1, pp 145, 2012. https://doi.org/10.1186/1752-0509-6-145
  19. Borate, B. R., et al., "Comparison of threshold selection methods for microarray gene co-expression matrices", BMC research notes, Vol. 2, No. 1, pp 240, 2009. https://doi.org/10.1186/1756-0500-2-240
  20. Faith, J.J., et al., "Large-scale mapping and validation of Escherichia coli transcriptional regulation from a compendium of expression profiles", Vol. 5, No. 1, pp e8, 2007.