Dataset Information

Learning virulent proteins from integrated query networks.

ABSTRACT: Methods of weakening and attenuating pathogens' abilities to infect and propagate in a host, thus allowing the natural immune system to more easily decimate invaders, have gained attention as alternatives to broad-spectrum targeting approaches. The following work describes a technique to identifying proteins involved in virulence by relying on latent information computationally gathered across biological repositories, applicable to both generic and specific virulence categories.A lightweight method for data integration is used, which links information regarding a protein via a path-based query graph. A method of weighting is then applied to query graphs that can serve as input to various statistical classification methods for discrimination, and the combined usage of both data integration and learning methods are tested against the problem of both generalized and specific virulence function prediction.This approach improves coverage of functional data over a protein. Moreover, while depending largely on noisy and potentially non-curated data from public sources, we find it outperforms other techniques to identification of general virulence factors and baseline remote homology detection methods for specific virulence categories.

SUBMITTER: Cadag E

PROVIDER: S-EPMC3560104 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature

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Learning virulent proteins from integrated query networks.

Cadag Eithon E Tarczy-Hornoch Peter P Myler Peter J PJ

BMC bioinformatics 20121202

<h4>Background</h4>Methods of weakening and attenuating pathogens' abilities to infect and propagate in a host, thus allowing the natural immune system to more easily decimate invaders, have gained attention as alternatives to broad-spectrum targeting approaches. The following work describes a technique to identifying proteins involved in virulence by relying on latent information computationally gathered across biological repositories, applicable to both generic and specific virulence categorie ...[more]

PMID: 23198735

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Project description:Background:Monitoring and control of PM2.5 are being recognized as key to address health issues attributed to PM2.5. Availability of low-cost PM2.5 sensors made it possible to introduce a number of portable PM2.5 monitors based on light scattering to the consumer market at an affordable price. Accuracy of light scattering-based PM2.5 monitors significantly depends on the method of calibration. Static calibration curve is used as the most popular calibration method for low-cost PM2.5 sensors particularly because of ease of application. Drawback in this approach is, however, the lack of accuracy. Methods:This study discussed the calibration of a low-cost PM2.5-monitoring device (PMD) to improve the accuracy and reliability for practical use. The proposed method is based on construction of the PM2.5 sensor network using Message Queuing Telemetry Transport (MQTT) protocol and web query of reference measurement data available at government-authorized PM monitoring station (GAMS) in the republic of Korea. Four machine learning (ML) algorithms such as support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting were used as regression models to calibrate the PMD measurements of PM2.5. Performance of each ML algorithm was evaluated using stratified K-fold cross-validation, and a linear regression model was used as a reference. Results:Based on the performance of ML algorithms used, regression of the output of the PMD to PM2.5 concentrations data available from the GAMS through web query was effective. The extreme gradient boosting algorithm showed the best performance with a mean coefficient of determination (R2) of 0.78 and standard error of 5.0 ?g/m3, corresponding to 8% increase in R2 and 12% decrease in root mean square error in comparison with the linear regression model. Minimum 100 hours of calibration period was found required to calibrate the PMD to its full capacity. Calibration method proposed poses a limitation on the location of the PMD being in the vicinity of the GAMS. As the number of the PMD participating in the sensor network increases, however, calibrated PMDs can be used as reference devices to nearby PMDs that require calibration, forming a calibration chain through MQTT protocol. Conclusions:Calibration of a low-cost PMD, which is based on construction of PM2.5 sensor network using MQTT protocol and web query of reference measurement data available at a GAMS, significantly improves the accuracy and reliability of a PMD, thereby making practical use of the low-cost PMD possible.

Dataset Information

Learning virulent proteins from integrated query networks.

Publications

Learning virulent proteins from integrated query networks.

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