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Date Range
2010 2017

Background Glioblastoma is the most aggressive primary central nervous tumor and carries a very poor prognosis. Invasion precludes effective treatment and virtually assures tumor recurrence. In the current study, we applied analytical and bioinformatics approaches to identify a set of microRNAs (miRs) from several different human glioblastoma cell lines that exhibit significant differential expression between migratory (edge) and migration-restricted (core) cell populations. The hypothesis of the study is that differential expression of miRs provides an epigenetic mechanism to drive cell migration and invasion. Results Our research data comprise gene expression values for a set of 805 human miRs collected from ...

Contributors
Bradley, Barrie, Loftus, Joseph C., Mielke, Clinton, et al.
Created Date
2014-01-18

Background The discovery of genetic associations is an important factor in the understanding of human illness to derive disease pathways. Identifying multiple interacting genetic mutations associated with disease remains challenging in studying the etiology of complex diseases. And although recently new single nucleotide polymorphisms (SNPs) at genes implicated in immune response, cholesterol/lipid metabolism, and cell membrane processes have been confirmed by genome-wide association studies (GWAS) to be associated with late-onset Alzheimer's disease (LOAD), a percentage of AD heritability continues to be unexplained. We try to find other genetic variants that may influence LOAD risk utilizing data mining methods. Methods Two ...

Contributors
Briones, Natalia, Dinu, Valentin, Arizona State University. College of Health Solutions. Department of Biomedical Informatics, et al.
Created Date
2012-01-25

Background Time series models can play an important role in disease prediction. Incidence data can be used to predict the future occurrence of disease events. Developments in modeling approaches provide an opportunity to compare different time series models for predictive power. Results We applied ARIMA and Random Forest time series models to incidence data of outbreaks of highly pathogenic avian influenza (H5N1) in Egypt, available through the online EMPRES-I system. We found that the Random Forest model outperformed the ARIMA model in predictive ability. Furthermore, we found that the Random Forest model is effective for predicting outbreaks of H5N1 in ...

Contributors
Kane, Michael J., Price, Natalie, Scotch, Matthew, et al.
Created Date
2014-08-13

Community associated methicillin-resistant Staphylococcus aureus (CA-MRSA) has become a major cause of skin and soft tissue infections (SSTIs) in the US. We developed an age-structured compartmental model to study the spread of CA-MRSA at the population level and assess the effect of control intervention strategies. We used Monte-Carlo Markov Chain (MCMC) techniques to parameterize our model using monthly time series data on SSTIs incidence in children (≤19 years) during January 2004 -December 2006 in Maricopa County, Arizona. Our model-based forecast for the period January 2007–December 2008 also provided a good fit to data. We also carried out an uncertainty and ...

Contributors
Wang, Xiaoxia, Panchanathan, Sarada, Chowell-Puente, Gerardo, et al.
Created Date
2013-11-21

Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, ...

Contributors
Noren, David P., Long, Byron L., Norel, Raquel, et al.
Created Date
2016-06-28

Background: Consumer eHealth tools play an increasingly important role in engaging patients as participants in managing their health and seeking health information. However, there is a documented gap between the skill and knowledge demands of eHealth systems and user competencies to benefit from these tools. Objective: This research aims to reveal the knowledge- and skill-related barriers to effective use of eHealth tools. Methods: We used a micro-analytic framework for characterizing the different cognitive dimensions of eHealth literacy to classify task demands and barriers that 20 participants experienced while performing online information-seeking and decision-making tasks. Results: Participants ranged widely in their ...

Contributors
Chan, Connie V., Mirkovic, Jelena, Furniss, Stephanie, et al.
Created Date
2015-12
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Emerging and re-emerging infectious diseases of zoonotic origin like highly pathogenic avian influenza pose a significant threat to human and animal health due to their elevated transmissibility. Identifying the drivers of such viruses is challenging, and estimation of spatial diffusion is complicated by the fact that the variability of viral spread from locations could be caused by a complex array of unknown factors. Several techniques exist to help identify these drivers, including bioinformatics, phylogeography, and spatial epidemiology, but these methods are generally evaluated separately and do not consider the complementary nature of each other. Here, we studied an approach that ...

Contributors
Magee, Daniel, Beard, Rachel, Suchard, Marc A., et al.
Created Date
2015-01-01

A key factor in the effectiveness of the seasonal influenza vaccine is its immunological compatibility with the circulating viruses during the season. Here we propose a new bioinformatics approach for analysis of influenza viruses which could be used as an efficient tool for selection of vaccine viruses, assessment of the effectiveness of seasonal influenza vaccines, and prediction of the epidemic/pandemic potential of novel influenza viruses.

Contributors
Veljkovic, Veljko, Paessler, Slobodan, Glisic, Sanja, et al.
Created Date
2015-12-22

Whole genome sequencing (WGS) is a promising strategy to unravel variants or genes responsible for human diseases and traits. However, there is a lack of robust platforms for a comprehensive downstream analysis. In the present study, we first proposed three novel algorithms, sequence gap-filled gene feature annotation, bit-block encoded genotypes and sectional fast access to text lines to address three fundamental problems. The three algorithms then formed the infrastructure of a robust parallel computing framework, KGGSeq, for integrating downstream analysis functions for whole genome sequencing data. KGGSeq has been equipped with a comprehensive set of analysis functions for quality control, ...

Contributors
Li, Miaoxin, Li, Jiang, Li, Mulin Jun, et al.
Created Date
2017-01-23

The diverse, specialized genes present in today’s lifeforms evolved from a common core of ancient, elementary genes. However, these genes did not evolve individually: gene expression is controlled by a complex network of interactions, and alterations in one gene may drive reciprocal changes in its proteins’ binding partners. Like many complex networks, these gene regulatory networks (GRNs) are composed of communities, or clusters of genes with relatively high connectivity. A deep understanding of the relationship between the evolutionary history of single genes and the topological properties of the underlying GRN is integral to evolutionary genetics. Here, we show that the ...

Contributors
Szedlak, Anthony, Smith, Nicholas, Liu, Li, et al.
Created Date
2016-06-30