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An integrated Bayesian network framework for reconstructing representative genetic regulatory networks
이필현 (KAIST)
제2차 한국생물정보학회 연례학술대회  |  2003.10.31
In this paper, we propose the integrated Bayesian network framework to reconstruct genetic regulatory
networks from genome expression data. The proposed model overcomes the dimensionality problem of
multivariate analysis by building coherent sub-networks from confined gene clusters and combining these networks via intermediary points. Gene Shaving algorithm is used to cluster genes that share a common
function or co-regulation. Retrieved clusters incorporate prior biological knowledge such as Gene
Ontology, pathway, and protein protein interaction information for extracting other related genes. With
these extended gene list, system builds genetic sub-networks using Bayesian network with MDL score
and Sparse Candidate algorithm. Identifying functional modules of genes is done by not only microarray
data itself but also well-proved biological knowledge. This integrated approach can improve the
reliability of a network in that false relations due to the lack of data can be reduced. Another advantage is the decreased computational complexity by constrained gene sets. To evaluate the proposed system, S.Cerevisiae cell cycle data [1] is applied. The result analysis presents new hypotheses about novel genetic interactions as well as typical relationships known by previous researches [2].
본 동영상의 Citation 복사
조회 2942    주소복사 트위터 공유 페이스북 공유 
Microarray and Gene Expression
제2차 한국생물정보학회 연례학술대회 | 2003.10.31
제2차 한국생물정보학회 연례학술대회 | 2003.10.31
제2차 한국생물정보학회 연례학술대회 | 2003.10.31
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