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Molecular studies of bacterial virulence are enhanced by expression of recombinant DNA during infection to allow complementation of mutants and expression of reporter proteins in vivo. For highly pathogenic bacteria, such as Yersinia pestis, these studies are currently limited because deliberate introduction of antibiotic resistance is restricted to those(More)
Bayesian networks (BNs) are useful for coding conditional independence statements, especially in discrete symmetric models. On the other hand, event trees (ETs) are convenient for representing asymmetric structure and how situations unfold. In this paper we report the development of a new graph-ical framework called the chain event graph (CEG). For(More)
We study the evolution of the network properties of a populated network embedded in a genotype space characterized by either a low or a high number of potential links, with particular emphasis on the connectivity and clustering. Evolution produces two distinct types of network. When a specific genotype is only able to influence a few other genotypes, the(More)
What features characterize complex system dynamics? Power laws and scale invariance of fluctuations are often taken as the hallmarks of complexity, drawing on analogies with equilibrium critical phenomena. Here we argue that slow, directed dynamics, during which the system's properties change significantly, is fundamental. The underlying dynamics is related(More)
Automated isolation of translational efficiency bias that resists the confounding effect of GC(AT)-content Amino acid cost and codon-usage biases in 6 prokaryotic genomes: A whole-genome analysis GA-facilitated knowledge discovery and pattern recognition optimization applied to the biochemistry of protein solvation
We describe Data Science, a four-year undergraduate program in predictive analytics, machine learning, and data mining implemented at the College of Charleston, Charleston, South Carolina, USA. We present a ten-year status report detailing the program's origins, successes, and challenges. Our experience demonstrates that education and training for big data(More)
In light of global reef decline new methods to accurately, cheaply, and quickly evaluate coral metabolic states are needed to assess reef health. Metabolomic profiling can describe the response of individuals to disturbance (i.e., shifts in environmental conditions) across biological models and is a powerful approach for characterizing and comparing coral(More)
BACKGROUND Next generation sequencing (NGS) is being widely used to identify genetic variants associated with human disease. Although the approach is cost effective, the underlying data is susceptible to many types of error. Importantly, since NGS technologies and protocols are rapidly evolving, with constantly changing steps ranging from sample preparation(More)
MOTIVATION Common contemporary practice within the nuclear magnetic resonance (NMR) metabolomics community is to evaluate and validate novel algorithms on empirical data or simplified simulated data. Empirical data captures the complex characteristics of experimental data, but the optimal or most correct analysis is unknown a priori; therefore, researchers(More)