TY - GEN
T1 - Knowledge-guided differential dependency network learning for detecting structural changes in biological networks
AU - Tian, Ye
AU - Zhang, Bai
AU - Shih, Ie Ming
AU - Wang, Yue
PY - 2011
Y1 - 2011
N2 - Rapid advances in high-throughput molecular profiling such as DNA microarrays have created unprecedented opportunities to unravel the mechanisms that orchestrate the activities of genes and proteins in cells, where reconstruction of condition-specific biological networks directly from data has attracted great interest. In parallel, significant efforts have also been made to manually curate molecular interactions in cells, such as protein-protein interactions and biological pathways, providing constantly accumulated rich domain knowledge. Novel incorporation of biological prior knowledge into network learning algorithms can effectively leverage domain knowledge and make data-driven inference more robust and biologically relevant. However, biological prior knowledge is neither condition-specific nor context-specific, only serving as an aggregated source of partially-validated evidence under diverse experimental conditions. Hence, direct incorporation of imperfect and non-specific prior knowledge in specific problems is prone to errors and may lead to false positives. To address this challenge, 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. We also propose a sampling scheme to estimate the expected error rate due to "random" knowledge and develop a strategy to manage such error in our algorithm that fully exploits the benefit of prior knowledge while remaining robust to the false positive edges in the knowledge. We test the proposed method on two simulation data sets and demonstrate the effectiveness of this method. The experimental results are consistent with our theoretical analysis. Finally, we apply our method to real ovarian cancer microarray data and obtain biologically plausible results.
AB - Rapid advances in high-throughput molecular profiling such as DNA microarrays have created unprecedented opportunities to unravel the mechanisms that orchestrate the activities of genes and proteins in cells, where reconstruction of condition-specific biological networks directly from data has attracted great interest. In parallel, significant efforts have also been made to manually curate molecular interactions in cells, such as protein-protein interactions and biological pathways, providing constantly accumulated rich domain knowledge. Novel incorporation of biological prior knowledge into network learning algorithms can effectively leverage domain knowledge and make data-driven inference more robust and biologically relevant. However, biological prior knowledge is neither condition-specific nor context-specific, only serving as an aggregated source of partially-validated evidence under diverse experimental conditions. Hence, direct incorporation of imperfect and non-specific prior knowledge in specific problems is prone to errors and may lead to false positives. To address this challenge, 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. We also propose a sampling scheme to estimate the expected error rate due to "random" knowledge and develop a strategy to manage such error in our algorithm that fully exploits the benefit of prior knowledge while remaining robust to the false positive edges in the knowledge. We test the proposed method on two simulation data sets and demonstrate the effectiveness of this method. The experimental results are consistent with our theoretical analysis. Finally, we apply our method to real ovarian cancer microarray data and obtain biologically plausible results.
KW - Biological networks
KW - Convex optimization
KW - Graphical models
KW - Network structural change detection
KW - Network structure learning
UR - http://www.scopus.com/inward/record.url?scp=84858962477&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84858962477&partnerID=8YFLogxK
U2 - 10.1145/2147805.2147833
DO - 10.1145/2147805.2147833
M3 - Conference contribution
AN - SCOPUS:84858962477
SN - 9781450307963
T3 - 2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011
SP - 254
EP - 263
BT - 2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011
T2 - 2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011
Y2 - 1 August 2011 through 3 August 2011
ER -