Gene Expression Data of Deletion Mutants In this table, each column represents a deletion mutant strain , and each row measures the expression changes of a specific gene , ‘1’ means up-regulation, ‘-1’ means down-regulation and ‘0’ means no specific change.
Inferring Genetic Regulatory Networks Our goal is to infer a genetic regulatory network among the Deletion mutant genes … However, traditional Bayesian network learning approaches failed… Why? It is because the dominant value in the deletion mutant gene expression data set is ‘0’, which quantity is magnitudes larger than the ‘1’ and ‘-1’ values. Using traditional BN-learning metrics, such as K2, BDeu, BIC/MDL, the huge intra-similarities between ‘0’s will overwhelm true regulatory signals….
The DM_BN Kernel To overcome this problem, we resort to kernel-based BN learning. To this end, we propose the DM_BN kernel. The key insight is to block the intra-similarities between ‘0’s:
Incorporating a priori causal information We also use a template matrix to incorporate the a priori knowledge from deletion-mutant experiments into BN learning. If Gene B is in the (influence) target list of Gene A, but not the reverse case , we set (i, j) = 1, (j, i) = 0 in the template matrix to prohibit the appearance of B->A in the BN. In this way, the template matrix constraints the set of plausible edges in a DAG. Finally, to convert a DAG to a PDAG after BN learning, we must Resort to Meek’s rules [Meek, 1995] to judge the reversibility of Each edge, but not Chickering’s algorithm [Chickering, 1995].
High quality of the networks inferred by DM_BN
Correctness of edge directions with/without using templates Without using the template matrix, DM_BN kernel leads to ~80% accuracy in the de novo inference of edge directionalities, which is statistically significant compared to random guessing.
The inferred Yeast regulatory network Online acyclicity checking is implemented to enable learning large networks.
Integrating Genomic, Epigenomic, and Transcriptomic Features Reveals Modular Signatures Underlying Poor Prognosis in Ovarian Cancer Thanks for Dr. Wei Zhang’s contribution to the slides on this topic. W Zhang, Y Liu et al., Cell Reports (2013)
T he C ancer G enome A tlas (TCGA)
Summary of the Ovarian cancer data in TCGA
Summary of the Ovarian cancer data in TCGA The copy number segmentation data were mapped to the positions of genes and miRNAs.
- Fall '19
- DNA, ovarian cancer, Bayesian network
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