11,167 research outputs found
On the design of diploid genetic algorithms for problem optimization in dynamic environments
Tihis article is posted here with permission from the IEEE - Copyright @ 2006 IEEEUsing diploidy and dominance is one method to enhance the performance of genetic algorithms in dynamic environments. For diploidy genetic algorithms, there are two key design factors: the cardinality of genotypic alleles and the uncertainty in the dominance scheme. This paper investigates the effect of these two factors on the performance of diploidy genetic algorithms in dynamic environments. A generalized diploidy and dominance scheme is proposed for diploidy genetic algorithms, where the cardinality of genotypic alleles and/or the uncertainty in the dominance scheme can be easily tuned and studied. The experimental results show the efficiency of increasing genotypic cardinality rather than introducing uncertainty in the dominance scheme
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Searching for improvement
Engineering design can be thought of as a search for the best solutions to engineering problems. To perform an effective search, one must distinguish between competing designs and establish a measure of design quality, or fitness. To compare different designs, their features must be adequately described in a well-defined framework, which can mean separating the creative and analytical parts of the design process. By this we mean that a distinction is drawn between coming up with novel design concepts, or architectures, and the process of detailing or refining existing design architecture. In the case of a given design architecture, one can consider the set of all possible designs that could be created by varying its features. If it were possible to measure the fitness of all designs in this set, then one could identify a fitness landscape and search for the best possible solution for this design architecture. In this Chapter, the significance of the interactions between design features in defining the metaphorical fitness landscape is described. This highlights that the efficiency of a search algorithm is inextricably linked to the problem structure (and hence the landscape). Two approaches, namely, Genetic Algorithms (GA) and Robust Engineering Design (RED) are considered in some detail with reference to a case study on improving the design of cardiovascular stents
Compressed Genotyping
Significant volumes of knowledge have been accumulated in recent years
linking subtle genetic variations to a wide variety of medical disorders from
Cystic Fibrosis to mental retardation. Nevertheless, there are still great
challenges in applying this knowledge routinely in the clinic, largely due to
the relatively tedious and expensive process of DNA sequencing. Since the
genetic polymorphisms that underlie these disorders are relatively rare in the
human population, the presence or absence of a disease-linked polymorphism can
be thought of as a sparse signal. Using methods and ideas from compressed
sensing and group testing, we have developed a cost-effective genotyping
protocol. In particular, we have adapted our scheme to a recently developed
class of high throughput DNA sequencing technologies, and assembled a
mathematical framework that has some important distinctions from 'traditional'
compressed sensing ideas in order to address different biological and technical
constraints.Comment: Submitted to IEEE Transaction on Information Theory - Special Issue
on Molecular Biology and Neuroscienc
Fast Identification of Biological Pathways Associated with a Quantitative Trait Using Group Lasso with Overlaps
Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within
biological pathways, the incorporation of prior pathways information into a
statistical model is expected to increase the power to detect true associations
in a genetic association study. Most existing pathways-based methods rely on
marginal SNP statistics and do not fully exploit the dependence patterns among
SNPs within pathways. We use a sparse regression model, with SNPs grouped into
pathways, to identify causal pathways associated with a quantitative trait.
Notable features of our "pathways group lasso with adaptive weights" (P-GLAW)
algorithm include the incorporation of all pathways in a single regression
model, an adaptive pathway weighting procedure that accounts for factors
biasing pathway selection, and the use of a bootstrap sampling procedure for
the ranking of important pathways. P-GLAW takes account of the presence of
overlapping pathways and uses a novel combination of techniques to optimise
model estimation, making it fast to run, even on whole genome datasets. In a
comparison study with an alternative pathways method based on univariate SNP
statistics, our method demonstrates high sensitivity and specificity for the
detection of important pathways, showing the greatest relative gains in
performance where marginal SNP effect sizes are small.Comment: 29 page
Routes for breaching and protecting genetic privacy
We are entering the era of ubiquitous genetic information for research,
clinical care, and personal curiosity. Sharing these datasets is vital for
rapid progress in understanding the genetic basis of human diseases. However,
one growing concern is the ability to protect the genetic privacy of the data
originators. Here, we technically map threats to genetic privacy and discuss
potential mitigation strategies for privacy-preserving dissemination of genetic
data.Comment: Draft for comment
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