39,809 research outputs found
Innovative in silico approaches to address avian flu using grid technology
The recent years have seen the emergence of diseases which have spread very
quickly all around the world either through human travels like SARS or animal
migration like avian flu. Among the biggest challenges raised by infectious
emerging diseases, one is related to the constant mutation of the viruses which
turns them into continuously moving targets for drug and vaccine discovery.
Another challenge is related to the early detection and surveillance of the
diseases as new cases can appear just anywhere due to the globalization of
exchanges and the circulation of people and animals around the earth, as
recently demonstrated by the avian flu epidemics. For 3 years now, a
collaboration of teams in Europe and Asia has been exploring some innovative in
silico approaches to better tackle avian flu taking advantage of the very large
computing resources available on international grid infrastructures. Grids were
used to study the impact of mutations on the effectiveness of existing drugs
against H5N1 and to find potentially new leads active on mutated strains. Grids
allow also the integration of distributed data in a completely secured way. The
paper presents how we are currently exploring how to integrate the existing
data sources towards a global surveillance network for molecular epidemiology.Comment: 7 pages, submitted to Infectious Disorders - Drug Target
INDEMICS: An Interactive High-Performance Computing Framework for Data Intensive Epidemic Modeling
We describe the design and prototype implementation of Indemics (_Interactive; Epi_demic; _Simulation;)—a modeling environment utilizing high-performance computing technologies for supporting complex epidemic simulations. Indemics can support policy analysts and epidemiologists interested in planning and control of pandemics. Indemics goes beyond traditional epidemic simulations by providing a simple and powerful way to represent and analyze policy-based as well as individual-based adaptive interventions. Users can also stop the simulation at any point, assess the state of the simulated system, and add additional interventions. Indemics is available to end-users via a web-based interface.
Detailed performance analysis shows that Indemics greatly enhances the capability and productivity of simulating complex intervention strategies with a marginal decrease in performance. We also demonstrate how Indemics was applied in some real case studies where complex interventions were implemented
A graph-theory method for pattern identification in geographical epidemiology - a preliminary application to deprivation and mortality
Background: Graph theoretical methods are extensively used in the field of computational chemistry to search datasets of compounds to see if they contain particular molecular substructures or patterns. We describe a preliminary application of a graph theoretical method, developed in computational chemistry, to geographical epidemiology in relation to testing a prior hypothesis. We tested the methodology on the hypothesis that if a socioeconomically deprived neighbourhood is situated in a wider deprived area, then that neighbourhood would experience greater adverse effects on mortality compared with a similarly deprived neighbourhood which is situated in a wider area with generally less deprivation.
Methods: We used the Trent Region Health Authority area for this study, which contained 10,665 census enumeration districts (CED). Graphs are mathematical representations of objects and their relationships and within the context of this study, nodes represented CEDs and edges were determined by whether or not CEDs were neighbours (shared a common boundary). The overall area in this study was represented by one large graph comprising all CEDs in the region, along with their adjacency information. We used mortality data from 1988-1998, CED level population estimates and the Townsend Material Deprivation Index as an indicator of neighbourhood level deprivation. We defined deprived CEDs as those in the top 20% most deprived in the Region. We then set out to classify these deprived CEDs into seven groups defined by increasing deprivation levels in the neighbouring CEDs. 506 (24.2%) of the deprived CEDs had five adjacent CEDs and we limited pattern development and searching to these CEDs. We developed seven query patterns and used the RASCAL (Rapid Similarity Calculator) program to carry out the search for each of the query patterns. This program used a maximum common subgraph isomorphism method which was modified to handle geographical data.
Results: Of the 506 deprived CEDs, 10 were not identified as belonging to any of the seven groups because they were adjacent to a CED with a missing deprivation category quintile, and none fell within query Group 1 (a deprived CED for which all five adjacent CEDs were affluent). Only four CEDs fell within Group 2, which was defined as having four affluent adjacent CEDs and one non-affluent adjacent CED. The numbers of CEDs in Groups 3-7 were 17, 214, 95, 81 and 85 respectively. Age and sex adjusted mortality rate ratios showed a non-significant trend towards increasing mortality risk across Groups (Chi-square = 3.26, df = 1, p = 0.07).
Conclusion: Graph theoretical methods developed in computational chemistry may be a useful addition to the current GIS based methods available for geographical epidemiology but further developmental work is required. An important requirement will be the development of methods for specifying multiple complex search patterns. Further work is also required to examine the utility of using distance, as opposed to adjacency, to describe edges in graphs, and to examine methods for pattern specification when the nodes have multiple attributes attached to them
Supporting Regularized Logistic Regression Privately and Efficiently
As one of the most popular statistical and machine learning models, logistic
regression with regularization has found wide adoption in biomedicine, social
sciences, information technology, and so on. These domains often involve data
of human subjects that are contingent upon strict privacy regulations.
Increasing concerns over data privacy make it more and more difficult to
coordinate and conduct large-scale collaborative studies, which typically rely
on cross-institution data sharing and joint analysis. Our work here focuses on
safeguarding regularized logistic regression, a widely-used machine learning
model in various disciplines while at the same time has not been investigated
from a data security and privacy perspective. We consider a common use scenario
of multi-institution collaborative studies, such as in the form of research
consortia or networks as widely seen in genetics, epidemiology, social
sciences, etc. To make our privacy-enhancing solution practical, we demonstrate
a non-conventional and computationally efficient method leveraging distributing
computing and strong cryptography to provide comprehensive protection over
individual-level and summary data. Extensive empirical evaluation on several
studies validated the privacy guarantees, efficiency and scalability of our
proposal. We also discuss the practical implications of our solution for
large-scale studies and applications from various disciplines, including
genetic and biomedical studies, smart grid, network analysis, etc
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