Among network modeling tasks, identifying the rewiring of network structure is particularly instrumental in revealing and pinpointing the molecular cause of a disease. Effective incorporation of biological prior knowledge into network learning algorithms can leverage domain knowledge and make data driven inference more robust and biologically relevant. We formulate the inference of condition specific network structures that incorporates relevant prior knowledge as a convex optimization problem, and develop an efficient learning algorithm to jointly infer the biological networks as well as their changes. We test the proposed method on simulation data sets and demonstrate the effectiveness of this method . We then apply our method to yeast cell line data and breast cancer microarray data and obtain biologically plausible results.
Modeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, the modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. We formulated the inference of differential dependency networks that incorporates both conditional data and prior knowledge as a convex optimization problem, and developed an efficient learning algorithm to jointly infer the conserved biological network and the significant rewiring across different conditions. We used a novel sampling scheme to estimate the expected error rate due to random knowledge and based on which, developed a strategy that fully exploits the benefit of this data-knowledge integrated approach. We demonstrated and validated the principle and performance of our method using synthetic datasets . We then applied our method to yeast cell line and breast cancer microarray data and obtained biologically plausible results.
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Among network modeling tasks, identifying the rewiring of network structure is particularly instrumental in revealing and pinpointing the molecular cause of a disease. Effective incorporation of biological prior knowledge into network learning algorithms can leverage domain knowledge and make data driven inference more robust and biologically relevant. We formulate
Modeling biological networks serves as both a major goal and an effective tool of systems biology in studying mechanisms that orchestrate the activities of gene products in cells. Biological networks are context specific and dynamic in nature. To systematically characterize the selectively activated regulatory components and mechanisms, the modeling tools must be able to effectively distinguish significant rewiring from random background fluctuations. We formulated
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condition specific network structures that incorporates relevant
differential dependency networks that incorporates both conditional data and
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develop
developed
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biological networks as well as their changes. We test the proposed method on simulation data sets and demonstrate the effectiveness of this method
conserved biological network and the significant rewiring across different conditions. We used a novel sampling scheme to estimate the expected error rate due to random knowledge and based on which, developed a strategy that fully exploits the benefit of this data-knowledge integrated approach. We demonstrated and validated the principle and performance of our method using synthetic datasets
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apply
applied
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data
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obtain
obtained