KSBB Journal

The Korean Society for Biotechnology and Bioengineering (한국생물공학회)
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Informatics Network Representation Between Cells Using Probabilistic Graphical Models

확률적 그래프 모델을 이용한 세포 간 정보 네트워크 추론

  • Ra, Sang-Dong (Department of Computer Engineering, College of Electronics and Information Engineering, Chosun University) ;
  • Shin, Hyun-Jae (Department of Chemical & Biochemical Engineering, Chosun University) ;
  • Cha, Wol-Suk (Department of Chemical & Biochemical Engineering, Chosun University)
  • 나상동 (조선대학교 전자정보공과대학 컴퓨터공학부) ;
  • 신현재 (조선대학교 공과대학 생명화학공학과) ;
  • 차월석 (조선대학교 공과대학 생명화학공학과)
  • Published : 2006.08.30
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Abstract

This study is a numerical representative modeling analysis for the application of the process that unravels networks between cells in genetics to web of informatics. Using the probabilistic graphical model, the insight from the data describing biological networks is used for making a probabilistic function. Rather than a complex network of cells, we reconstruct a simple lower-stage model and show a genetic representation level from the genetic based network logic. We made probabilistic graphical models from genetic data and extends them to genetic representation data in the method of network modeling in informatics

유전자 생물학 분야에서 적용가능 한 세포간 네트워크를 입증하는 고처리 정보공학에 응용하려는 수치학적인 표현 모델을 분석 연구한다. 확률적 그래프 모델을 사용하여 데이터 네트워크로부터 생물학적 통찰력을 확률적 함수적으로 응용해 복잡한 세포간 네트워크보다 단순한 하부모델로 구성하여 유전자 베이스네트워크 논리를 유전자 표현 레벨로 나타낸다. 유전자 데이터로부터 확률적 그래프 모델들을 분석하여 유전자 표현 데이터를 정보공학 네트워크 모델의 방법으로 확장 추론한다.

Keywords

References

  1. Lander, E. (1999), Array of hope, Nature Genet. 21, 3-4 https://doi.org/10.1038/4427
  2. Pilpel, Y., P. Sudarsanam, and G. Church (2001), Identifying regulatory networks by combinatorial analysis of promoter elements, Nature Genet. 29, 153-159 https://doi.org/10.1038/ng724
  3. Pearl, J. (2005), Probabilistic reasoning in intelligent systems: networks of plausible inference, Morgan Kaufmann, San Francisco
  4. Buntine, W. (1994), Operations for learning with graphical models. J. Artif. Intel. Res. 2, 159-225
  5. Getoor, L., N. Friedman, D. Koller, and A. Pfeffer (2001), In Relational Data Mining, Learning Probabilistic Relational Models, S. Dzeroski and N. Lavrac, Eds., pp.307-337. Springer-Verlag, Berlin
  6. Segal, E., B. Taskar, A. Gasch, N. Friedman, and D. Koller (2001), Rich probabilistic models for gene expression, Bioinformatics 17(suppl. 1), S243 https://doi.org/10.1093/bioinformatics/17.suppl_1.S243
  7. Barash, Y. and N. Friedman (2002), Context-specific Bayesian clustering for gene expression data, J. Comp. Biol. 9, 169-191 https://doi.org/10.1089/10665270252935403
  8. Holmes, I. and W. Bruno (2000), Finding regulatory elements using joint likelihoods for sequence and expression profile data, Proc. Int. Conf. Intell. Syst. Mol. Biol. 8, 202-210
  9. Segal, E., R. Yelensky, and D. Koller (2003), Genome-wide discovery of transcriptional modules from DNA sequence and gene expression, Bioinformatics 19(suppl. 1), I273- I282 https://doi.org/10.1093/bioinformatics/btg1038
  10. Gasch, A. P. et al. (2000), Genomic expression programs in the response of yeast cells to environmental changes, Mol. Biol. Cell 11, 4241-57 https://doi.org/10.1091/mbc.11.12.4241
  11. Lee, T. et al. (2002), Transcriptional regulatory networks in Saccharomyces cerevisiae, Science 298, 799-804 https://doi.org/10.1126/science.1075090