Learning Gene Regulatory Networks Computationally from Gene Expression Data Using Weighted Consensus

Learning Gene Regulatory Networks Computationally from Gene Expression Data Using Weighted Consensus

Gene regulatory networks analyze the relationships between genes allowing us to un- derstand the gene regulatory interactions in systems biology. Gene expression data from the microarray experiments is used to obtain the gene regulatory networks. How- ever, the microarray data is discrete, noisy and...

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Bibliographic Details
Main Author: Fujii, Chisato
Other Authors: Gao, Xin
Language:en
Published: 2015
Subjects:
gene regulatory networks
consensus
linear programming
Online Access:Fujii, C. (2015). Learning Gene Regulatory Networks Computationally from Gene Expression Data Using Weighted Consensus. KAUST Research Repository. https://doi.org/10.25781/KAUST-1A8VV
http://hdl.handle.net/10754/550417

Internet

Fujii, C. (2015). Learning Gene Regulatory Networks Computationally from Gene Expression Data Using Weighted Consensus. KAUST Research Repository. https://doi.org/10.25781/KAUST-1A8VV
http://hdl.handle.net/10754/550417

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