Bootstrapping Time-Course Gene Expression Data for Gene Networks: Application to Gene Relevance Networks.

Identification of gene regulatory networks (GRNs) is a fundamental step to understand the molecular role of each gene and it helps to develop treatment and cure of a disease. To identify GRNs, time-course gene expression data are widely used. However, the identification is hampered by intrinsic attributes of the data such as small sample size, a large number of variables, and complex error structures with high variation. Under this situation, most GRN inference methods utilize point estimators or make numerous assumptions that are often incompatible with the experimental data. Moreover, different inference methods often provide inconsistent results. An alternative to alleviate this problem can be the bootstrap method because it provides more reliable outcomes by integrating results from multiple bootstrap samples without any distributional assumptions. In this study, we propose a bootstrap method for dependent time-course gene expression data and we mainly focus on its application to gene relevance networks. The proposed method is applied to gene networks for zebrafish retina.

Non-obvious correlations to disease management unraveled by Bayesian artificial intelligence analyses of CMS data.

OBJECTIVE: Given the availability of extensive digitized healthcare data from medical records, claims and prescription information, it is now possible to use hypothesis-free, data-driven approaches to mine medical databases for novel insight. The goal of this analysis was to demonstrate the use of artificial intelligence based methods such as Bayesian networks to open up opportunities for creation of new knowledge in management of chronic conditions. MATERIALS AND METHODS: Hospital level Medicare claims data containing discharge numbers for most common diagnoses were analyzed in a hypothesis-free manner using Bayesian networks learning methodology. RESULTS: While many interactions identified between discharge rates of diagnoses using this data set are supported by current medical knowledge, a novel interaction linking asthma and renal failure was discovered. This interaction is non-obvious and had not been looked at by the research and clinical communities in epidemiological or clinical data. A plausible pharmacological explanation of this link is proposed together with a verification of the risk significance by conventional statistical analysis. CONCLUSION: Potential clinical and molecular pathways defining the relationship between commonly used asthma medications and renal disease are discussed. The study underscores the need for further epidemiological research to validate this novel hypothesis. Validation will lead to advancement in clinical treatment of asthma & bronchitis, thereby, improving patient outcomes and leading to long term cost savings. In summary, this study demonstrates that application of advanced artificial intelligence methods in healthcare has the potential to enhance the quality of care by discovering non-obvious, clinically relevant relationships and enabling timely care intervention.

Label-free detection of micro-RNA hybridization using surface-enhanced Raman spectroscopy and least-squares analysis.

Label-free surface-enhanced Raman spectroscopy (SERS) detection of nucleic acid hybridization is impeded by poor spectral reproducibility and the fact that the chemical signatures of hybridized and unhybridized sequences are highly similar. To overcome these issues, highly reproducible silver nanorod SERS substrates along with a straightforward least-squares (LS) technique have been employed for the quantitative determination of the relative ratios of the four nucleotide components A, C, G, and T/U before and after hybridization using a clinically relevant micro-RNA sequence.

Dynamic rastering surface-enhanced Raman scattering (SERS) measurements on silver nanorod substrates.

Surface-enhanced Raman spectra of a thiol-modified biotin derivative on oblique-angle-deposited silver nanorod (AgNR) array substrates were measured using both static and rotating rastering methods. We find that the rotating rastering method has a strong tendency to decrease the point-to-point relative standard deviation (RSD) compared to static measurements as well as decrease the effects of cumulative excitation exposure. The AgNR substrates treated with the modified biotin typically demonstrate intra-substrate RSDs of <10%, with an average RSD of ∼3% when the rastering radius r=1 mm. The quantitative studies on the relationship between rastering radius, sampling area, and rastering frequency show that only the rastering radius appears to have significant effect on the measured RSD. Our results demonstrate that under the proper measurement and sample preparation conditions, the Ag nanorod substrates are very uniform.

Identification of a protein subset of the anthrax spore immunome in humans immunized with the anthrax vaccine adsorbed preparation.

We identified spore targets of Anthrax Vaccine Adsorbed (AVA)-induced immunity in humans by screening recombinant clones of a previously generated, limited genomic Bacillus anthracis Sterne (pXO1(+), pXO2(-)) expression library of putative spore surface (spore-associated [SA]) proteins with pooled sera from human adults immunized with AVA (immune sera), the anthrax vaccine currently approved for use by humans in the United States. We identified 69 clones that reacted specifically with pooled immune sera but not with pooled sera obtained from the same individuals prior to immunization. Positive clones expressed proteins previously identified as localized on the anthrax spore surface, proteins highly expressed during spore germination, orthologs of proteins of diverse pathogens under investigation as drug targets, and orthologs of proteins contributing to the virulence of both gram-positive and gram-negative pathogens. Among the reactive clones identified by this immunological screen was one expressing a 15.2-kDa hypothetical protein encoded by a gene with no significant homology to sequences contained in databases. Further studies are required to define the subset of SA proteins identified in this study that contribute to the virulence of this pathogen. We hypothesize that optimal delivery of a subset of SA proteins identified by such studies to the immune system in combination with protective antigen (PA), the principal immunogen in AVA, might facilitate the development of defined, nonreactogenic, more-efficacious PA-based anthrax vaccines. Future studies might also facilitate the identification of SA proteins with potential to serve as targets for drug design, spore inactivation, or spore detection strategies.