Quantitative utilization of prior biological knowledge in the Bayesian network modeling of gene expression data

<p>Abstract</p> <p>Background</p> <p>Bayesian Network (BN) is a powerful approach to reconstructing genetic regulatory networks from gene expression data. However, expression data by itself suffers from high noise and lack of power. Incorporating prior biological knowledge can improve the performance. As each type of prior knowledge on its own may be incomplete or limited by quality issues, integrating multiple sources of prior knowledge to utilize their consensus is desirable.</p> <p>Results</p> <p>We introduce a new method to incorporate the quantitative information from multiple sources of prior knowledge. It first uses the Naïve Bayesian classifier to assess the likelihood of functional linkage between gene pairs based on prior knowledge. In this study we included cocitation in PubMed and schematic similarity in Gene Ontology annotation. A candidate network edge reservoir is then created in which the copy number of each edge is proportional to the estimated likelihood of linkage between the two corresponding genes. In network simulation the Markov Chain Monte Carlo sampling algorithm is adopted, and samples from this reservoir at each iteration to generate new candidate networks. We evaluated the new algorithm using both simulated and real gene expression data including that from a yeast cell cycle and a mouse pancreas development/growth study. Incorporating prior knowledge led to a ~2 fold increase in the number of known transcription regulations recovered, without significant change in false positive rate. In contrast, without the prior knowledge BN modeling is not always better than a random selection, demonstrating the necessity in network modeling to supplement the gene expression data with additional information.</p> <p>Conclusion</p> <p>our new development provides a statistical means to utilize the quantitative information in prior biological knowledge in the BN modeling of gene expression data, which significantly improves the performance.</p

The expression and role of macrophage migration inhibitory factor in hepatocellular carcinoma

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Polymeric nanoparticle vaccines to combat emerging and pandemic threats

Subunit vaccines are more advantageous than live attenuated vaccines in terms of safety and scale-up manufacture. However, this often comes as a trade-off to their efficacy. Over the years, polymeric nanoparticles have been developed to improve vaccine potency, by engineering their physicochemical properties to incorporate multiple immunological cues to mimic pathogenic microbes and viruses. This review covers recent advances in polymeric nanostructures developed toward particulate vaccines. It focuses on the impact of microbe mimicry (e.g. size, charge, hydrophobicity, and surface chemistry) on modulation of the nanoparticles’ delivery, trafficking, and targeting antigen-presenting cells to elicit potent humoral and cellular immune responses. This review also provides up-to-date progresses on rational designs of a wide variety of polymeric nanostructures that are loaded with antigens and immunostimulatory molecules, ranging from particles, micelles, nanogels, and polymersomes to advanced core-shell structures where polymeric particles are coated with lipids, cell membranes, or proteins.No Full Tex